Domain incremental learning aims to adapt to a sequence of domains with access to only a small subset of data (i.e., memory) from previous domains. Various methods have been proposed for this problem, but it is still unclear how they are related and when practitioners should choose one method over another. In response, we propose a unified framework, dubbed Unified Domain Incremental Learning (UDIL), for domain incremental learning with memory. Our UDIL **unifies** various existing methods, and our theoretical analysis shows that UDIL always achieves a tighter generalization error bound compared to these methods. The key insight is that different existing methods correspond to our bound with different **fixed** coefficients; based on insights from this unification, our UDIL allows **adaptive** coefficients during training, thereby always achieving the tightest bound. Empirical results show that our UDIL outperforms the state-of-the-art domain incremental learning methods on both synthetic and real-world datasets. Code will be available at https://github.com/Wang-ML-Lab/unified-continual-learning.  ( 2 min )

Significant computational resources are required to train Graph Neural Networks (GNNs) at a large scale, and the process is highly data-intensive. One of the most effective ways to reduce resource requirements is minibatch training coupled with graph sampling. GNNs have the unique property that items in a minibatch have overlapping data. However, the commonly implemented Independent Minibatching approach assigns each Processing Element (PE) its own minibatch to process, leading to duplicated computations and input data access across PEs. This amplifies the Neighborhood Explosion Phenomenon (NEP), which is the main bottleneck limiting scaling. To reduce the effects of NEP in the multi-PE setting, we propose a new approach called Cooperative Minibatching. Our approach capitalizes on the fact that the size of the sampled subgraph is a concave function of the batch size, leading to significant reductions in the amount of work per seed vertex as batch sizes increase. Hence, it is favorable for processors equipped with a fast interconnect to work on a large minibatch together as a single larger processor, instead of working on separate smaller minibatches, even though global batch size is identical. We also show how to take advantage of the same phenomenon in serial execution by generating dependent consecutive minibatches. Our experimental evaluations show up to 4x bandwidth savings for fetching vertex embeddings, by simply increasing this dependency without harming model convergence. Combining our proposed approaches, we achieve up to 64% speedup over Independent Minibatching on single-node multi-GPU systems.  ( 3 min )

Pretrained transformer models have demonstrated remarkable performance across various natural language processing tasks. These models leverage the attention mechanism to capture long- and short-range dependencies in the sequence. However, the (full) attention mechanism incurs high computational cost - quadratic in the sequence length, which is not affordable in tasks with long sequences, e.g., inputs with 8k tokens. Although sparse attention can be used to improve computational efficiency, as suggested in existing work, it has limited modeling capacity and often fails to capture complicated dependencies in long sequences. To tackle this challenge, we propose MASFormer, an easy-to-implement transformer variant with Mixed Attention Spans. Specifically, MASFormer is equipped with full attention to capture long-range dependencies, but only at a small number of layers. For the remaining layers, MASformer only employs sparse attention to capture short-range dependencies. Our experiments on natural language modeling and generation tasks show that a decoder-only MASFormer model of 1.3B parameters can achieve competitive performance to vanilla transformers with full attention while significantly reducing computational cost (up to 75%). Additionally, we investigate the effectiveness of continual training with long sequence data and how sequence length impacts downstream generation performance, which may be of independent interest.  ( 2 min )

In theoretical ML, the teacher-student paradigm is often employed as an effective metaphor for real-life tuition. The above scheme proves particularly relevant when the student network is overparameterized as compared to the teacher network. Under these operating conditions, it is tempting to speculate that the student ability to handle the given task could be eventually stored in a sub-portion of the whole network. This latter should be to some extent reminiscent of the frozen teacher structure, according to suitable metrics, while being approximately invariant across different architectures of the student candidate network. Unfortunately, state-of-the-art conventional learning techniques could not help in identifying the existence of such an invariant subnetwork, due to the inherent degree of non-convexity that characterizes the examined problem. In this work, we take a leap forward by proposing a radically different optimization scheme which builds on a spectral representation of the linear transfer of information between layers. The gradient is hence calculated with respect to both eigenvalues and eigenvectors with negligible increase in terms of computational and complexity load, as compared to standard training algorithms. Working in this framework, we could isolate a stable student substructure, that mirrors the true complexity of the teacher in terms of computing neurons, path distribution and topological attributes. When pruning unimportant nodes of the trained student, as follows a ranking that reflects the optimized eigenvalues, no degradation in the recorded performance is seen above a threshold that corresponds to the effective teacher size. The observed behavior can be pictured as a genuine second-order phase transition that bears universality traits.  ( 3 min )

Textural Inversion, a prompt learning method, learns a singular embedding for a new "word" to represent image style and appearance, allowing it to be integrated into natural language sentences to generate novel synthesised images. However, identifying and integrating multiple object-level concepts within one scene poses significant challenges even when embeddings for individual concepts are attainable. This is further confirmed by our empirical tests. To address this challenge, we introduce a framework for Multi-Concept Prompt Learning (MCPL), where multiple new "words" are simultaneously learned from a single sentence-image pair. To enhance the accuracy of word-concept correlation, we propose three regularisation techniques: Attention Masking (AttnMask) to concentrate learning on relevant areas; Prompts Contrastive Loss (PromptCL) to separate the embeddings of different concepts; and Bind adjective (Bind adj.) to associate new "words" with known words. We evaluate via image generation, editing, and attention visualisation with diverse images. Extensive quantitative comparisons demonstrate that our method can learn more semantically disentangled concepts with enhanced word-concept correlation. Additionally, we introduce a novel dataset and evaluation protocol tailored for this new task of learning object-level concepts.  ( 2 min )

Bilateral trade revolves around the challenge of facilitating transactions between two strategic agents -- a seller and a buyer -- both of whom have a private valuations for the item. We study the online version of the problem, in which at each time step a new seller and buyer arrive. The learner's task is to set a price for each agent, without any knowledge about their valuations. The sequence of sellers and buyers is chosen by an oblivious adversary. In this setting, known negative results rule out the possibility of designing algorithms with sublinear regret when the learner has to guarantee budget balance for each iteration. In this paper, we introduce the notion of global budget balance, which requires the agent to be budget balance only over the entire time horizon. By requiring global budget balance, we provide the first no-regret algorithms for bilateral trade with adversarial inputs under various feedback models. First, we show that in the full-feedback model the learner can guarantee $\tilde{O}(\sqrt{T})$ regret against the best fixed prices in hindsight, which is order-wise optimal. Then, in the case of partial feedback models, we provide an algorithm guaranteeing a $\tilde{O}(T^{3/4})$ regret upper bound with one-bit feedback, which we complement with a nearly-matching lower bound. Finally, we investigate how these results vary when measuring regret using an alternative benchmark.  ( 2 min )

The growing adoption of declarative software specification languages, coupled with their inherent difficulty in debugging, has underscored the need for effective and automated repair techniques applicable to such languages. Researchers have recently explored various methods to automatically repair declarative software specifications, such as template-based repair, feedback-driven iterative repair, and bounded exhaustive approaches. The latest developments in large language models provide new opportunities for the automatic repair of declarative specifications. In this study, we assess the effectiveness of utilizing OpenAI's ChatGPT to repair software specifications written in the Alloy declarative language. Unlike imperative languages, specifications in Alloy are not executed but rather translated into logical formulas and evaluated using backend constraint solvers to identify specification instances and counterexamples to assertions. Our evaluation focuses on ChatGPT's ability to improve the correctness and completeness of Alloy declarative specifications through automatic repairs. We analyze the results produced by ChatGPT and compare them with those of leading automatic Alloy repair methods. Our study revealed that while ChatGPT falls short in comparison to existing techniques, it was able to successfully repair bugs that no other technique could address. Our analysis also identified errors in ChatGPT's generated repairs, including improper operator usage, type errors, higher-order logic misuse, and relational arity mismatches. Additionally, we observed instances of hallucinations in ChatGPT-generated repairs and inconsistency in its results. Our study provides valuable insights for software practitioners, researchers, and tool builders considering ChatGPT for declarative specification repairs.  ( 3 min )

We address the challenge of tracking an unknown number of targets in strong clutter environments using measurements from a radar sensor. Leveraging the range-Doppler spectra information, we identify the measurement classes, which serve as additional information to enhance clutter rejection and data association, thus bolstering the robustness of target tracking. We first introduce a novel neural enhanced message passing approach, where the beliefs obtained by the unified message passing are fed into the neural network as additional information. The output beliefs are then utilized to refine the original beliefs. Then, we propose a classification-aided robust multiple target tracking algorithm, employing the neural enhanced message passing technique. This algorithm is comprised of three modules: a message-passing module, a neural network module, and a Dempster-Shafer module. The message-passing module is used to represent the statistical model by the factor graph and infers target kinematic states, visibility states, and data associations based on the spatial measurement information. The neural network module is employed to extract features from range-Doppler spectra and derive beliefs on whether a measurement is target-generated or clutter-generated. The Dempster-Shafer module is used to fuse the beliefs obtained from both the factor graph and the neural network. As a result, our proposed algorithm adopts a model-and-data-driven framework, effectively enhancing clutter suppression and data association, leading to significant improvements in multiple target tracking performance. We validate the effectiveness of our approach using both simulated and real data scenarios, demonstrating its capability to handle challenging tracking scenarios in practical radar applications.  ( 3 min )

With the development of large language models (LLMs), striking a balance between the performance and safety of AI systems has never been more critical. However, the inherent tension between the objectives of helpfulness and harmlessness presents a significant challenge during LLM training. To address this issue, we propose Safe Reinforcement Learning from Human Feedback (Safe RLHF), a novel algorithm for human value alignment. Safe RLHF explicitly decouples human preferences regarding helpfulness and harmlessness, effectively avoiding the crowdworkers' confusion about the tension and allowing us to train separate reward and cost models. We formalize the safety concern of LLMs as an optimization task of maximizing the reward function while satisfying specified cost constraints. Leveraging the Lagrangian method to solve this constrained problem, Safe RLHF dynamically adjusts the balance between the two objectives during fine-tuning. Through a three-round fine-tuning using Safe RLHF, we demonstrate a superior ability to mitigate harmful responses while enhancing model performance compared to existing value-aligned algorithms. Experimentally, we fine-tuned the Alpaca-7B using Safe RLHF and aligned it with collected human preferences, significantly improving its helpfulness and harmlessness according to human evaluations.  ( 2 min )

Pulsar timing arrays (PTAs) perform Bayesian posterior inference with expensive MCMC methods. Given a dataset of ~10-100 pulsars and O(10^3) timing residuals each, producing a posterior distribution for the stochastic gravitational wave background (SGWB) can take days to a week. The computational bottleneck arises because the likelihood evaluation required for MCMC is extremely costly when considering the dimensionality of the search space. Fortunately, generating simulated data is fast, so modern simulation-based inference techniques can be brought to bear on the problem. In this paper, we demonstrate how conditional normalizing flows trained on simulated data can be used for extremely fast and accurate estimation of the SGWB posteriors, reducing the sampling time from weeks to a matter of seconds.  ( 2 min )

Among the many variants of graph neural network (GNN) architectures capable of modeling data with cross-instance relations, an important subclass involves layers designed such that the forward pass iteratively reduces a graph-regularized energy function of interest. In this way, node embeddings produced at the output layer dually serve as both predictive features for solving downstream tasks (e.g., node classification) and energy function minimizers that inherit desirable inductive biases and interpretability. However, scaling GNN architectures constructed in this way remains challenging, in part because the convergence of the forward pass may involve models with considerable depth. To tackle this limitation, we propose a sampling-based energy function and scalable GNN layers that iteratively reduce it, guided by convergence guarantees in certain settings. We also instantiate a full GNN architecture based on these designs, and the model achieves competitive accuracy and scalability when applied to the largest publicly-available node classification benchmark exceeding 1TB in size.  ( 2 min )

Score-based generative models (SGMs) sample from a target distribution by iteratively transforming noise using the score function of the perturbed target. For any finite training set, this score function can be evaluated in closed form, but the resulting SGM memorizes its training data and does not generate novel samples. In practice, one approximates the score by training a neural network via score-matching. The error in this approximation promotes generalization, but neural SGMs are costly to train and sample, and the effective regularization this error provides is not well-understood theoretically. In this work, we instead explicitly smooth the closed-form score to obtain an SGM that generates novel samples without training. We analyze our model and propose an efficient nearest-neighbor-based estimator of its score function. Using this estimator, our method achieves sampling times competitive with neural SGMs while running on consumer-grade CPUs.  ( 2 min )

The burdensome impact of a skewed judges-to-cases ratio on the judicial system manifests in an overwhelming backlog of pending cases alongside an ongoing influx of new ones. To tackle this issue and expedite the judicial process, the proposition of an automated system capable of suggesting case outcomes based on factual evidence and precedent from past cases gains significance. This research paper centres on developing a graph neural network-based model to address the Legal Judgment Prediction (LJP) problem, recognizing the intrinsic graph structure of judicial cases and making it a binary node classification problem. We explored various embeddings as model features, while nodes such as time nodes and judicial acts were added and pruned to evaluate the model's performance. The study is done while considering the ethical dimension of fairness in these predictions, considering gender and name biases. A link prediction task is also conducted to assess the model's proficiency in anticipating connections between two specified nodes. By harnessing the capabilities of graph neural networks and incorporating fairness analyses, this research aims to contribute insights towards streamlining the adjudication process, enhancing judicial efficiency, and fostering a more equitable legal landscape, ultimately alleviating the strain imposed by mounting case backlogs. Our best-performing model with XLNet pre-trained embeddings as its features gives the macro F1 score of 75% for the LJP task. For link prediction, the same set of features is the best performing giving ROC of more than 80%

Spider silks are remarkable materials characterized by superb mechanical properties such as strength, extensibility and lightweightedness. Yet, to date, limited models are available to fully explore sequence-property relationships for analysis and design. Here we propose a custom generative large-language model to enable design of novel spider silk protein sequences to meet complex combinations of target mechanical properties. The model, pretrained on a large set of protein sequences, is fine-tuned on ~1,000 major ampullate spidroin (MaSp) sequences for which associated fiber-level mechanical properties exist, to yield an end-to-end forward and inverse generative strategy. Performance is assessed through: (1), a novelty analysis and protein type classification for generated spidroin sequences through BLAST searches, (2) property evaluation and comparison with similar sequences, (3) comparison of molecular structures, as well as, and (4) a detailed sequence motif analyses. We generate silk sequences with property combinations that do not exist in nature, and develop a deep understanding the mechanistic roles of sequence patterns in achieving overarching key mechanical properties (elastic modulus, strength, toughness, failure strain). The model provides an efficient approach to expand the silkome dataset, facilitating further sequence-structure analyses of silks, and establishes a foundation for synthetic silk design and optimization.

Given the ubiquitous use of tabular data in industries and the growing concerns in data privacy and security, tabular data synthesis emerges as a critical research area. The recent state-of-the-art methods show that large language models (LLMs) can be adopted to generate realistic tabular data. As LLMs pre-process tabular data as full text, they have the advantage of avoiding the curse of dimensionality associated with one-hot encoding high-dimensional data. However, their long training time and limited re-usability on new tasks prevent them from replacing exiting tabular generative models. In this paper, we propose Tabula, a tabular data synthesizer based on the language model structure. Through Tabula, we demonstrate the inherent limitation of employing pre-trained language models designed for natural language processing (NLP) in the context of tabular data synthesis. Our investigation delves into the development of a dedicated foundational model tailored specifically for tabular data synthesis. Additionally, we propose a token sequence compression strategy to significantly reduce training time while preserving the quality of synthetic data. Extensive experiments on six datasets demonstrate that using a language model structure without loading the well-trained model weights yields a better starting model for tabular data synthesis. Moreover, the Tabula model, previously trained on other tabular data, serves as an excellent foundation model for new tabular data synthesis tasks. Additionally, the token sequence compression method substantially reduces the model's training time. Results show that Tabula averagely reduces 46.2% training time per epoch comparing to current LLMs-based state-of-the-art algorithm and consistently achieves even higher synthetic data utility.

We study a distributed multi-armed bandit setting among a population of $n$ memory-constrained nodes in the gossip model: at each round, every node locally adopts one of $m$ arms, observes a reward drawn from the arm's (adversarially chosen) distribution, and then communicates with a randomly sampled neighbor, exchanging information to determine its policy in the next round. We introduce and analyze several families of dynamics for this task that are decentralized: each node's decision is entirely local and depends only on its most recently obtained reward and that of the neighbor it sampled. We show a connection between the global evolution of these decentralized dynamics with a certain class of "zero-sum" multiplicative weights update algorithms, and we develop a general framework for analyzing the population-level regret of these natural protocols. Using this framework, we derive sublinear regret bounds under a wide range of parameter regimes (i.e., the size of the population and number of arms) for both the stationary reward setting (where the mean of each arm's distribution is fixed over time) and the adversarial reward setting (where means can vary over time). Further, we show that these protocols can approximately optimize convex functions over the simplex when the reward distributions are generated from a stochastic gradient oracle.

Link prediction is a common task on graph-structured data that has seen applications in a variety of domains. Classically, hand-crafted heuristics were used for this task. Heuristic measures are chosen such that they correlate well with the underlying factors related to link formation. In recent years, a new class of methods has emerged that combines the advantages of message-passing neural networks (MPNN) and heuristics methods. These methods perform predictions by using the output of an MPNN in conjunction with a "pairwise encoding" that captures the relationship between nodes in the candidate link. They have been shown to achieve strong performance on numerous datasets. However, current pairwise encodings often contain a strong inductive bias, using the same underlying factors to classify all links. This limits the ability of existing methods to learn how to properly classify a variety of different links that may form from different factors. To address this limitation, we propose a new method, LPFormer, which attempts to adaptively learn the pairwise encodings for each link. LPFormer models the link factors via an attention module that learns the pairwise encoding that exists between nodes by modeling multiple factors integral to link prediction. Extensive experiments demonstrate that LPFormer can achieve SOTA performance on numerous datasets while maintaining efficiency.

Knowledge graph embedding models (KGEMs) have gained considerable traction in recent years. These models learn a vector representation of knowledge graph entities and relations, a.k.a. knowledge graph embeddings (KGEs). Learning versatile KGEs is desirable as it makes them useful for a broad range of tasks. However, KGEMs are usually trained for a specific task, which makes their embeddings task-dependent. In parallel, the widespread assumption that KGEMs actually create a semantic representation of the underlying entities and relations (e.g., project similar entities closer than dissimilar ones) has been challenged. In this work, we design heuristics for generating protographs -- small, modified versions of a KG that leverage RDF/S information. The learnt protograph-based embeddings are meant to encapsulate the semantics of a KG, and can be leveraged in learning KGEs that, in turn, also better capture semantics. Extensive experiments on various evaluation benchmarks demonstrate the soundness of this approach, which we call Modular and Agnostic SCHema-based Integration of protograph Embeddings (MASCHInE). In particular, MASCHInE helps produce more versatile KGEs that yield substantially better performance for entity clustering and node classification tasks. For link prediction, using MASCHinE substantially increases the number of semantically valid predictions with equivalent rank-based performance.

Knitted sensors frequently suffer from inconsistencies due to innate effects such as offset, relaxation, and drift. These properties, in combination, make it challenging to reliably map from sensor data to physical actuation. In this paper, we demonstrate a method for counteracting this by applying processing using a minimal artificial neural network (ANN) in combination with straightforward pre-processing. We apply a number of exponential smoothing filters on a re-sampled sensor signal, to produce features that preserve different levels of historical sensor data and, in combination, represent an adequate state of previous sensor actuation. By training a three-layer ANN with a total of 8 neurons, we manage to significantly improve the mapping between sensor reading and actuation force. Our findings also show that our technique translates to sensors of reasonably different composition in terms of material and structure, and it can furthermore be applied to related physical features such as strain.

In an era where symbolic mathematical equations are indispensable for modeling complex natural phenomena, scientific inquiry often involves collecting observations and translating them into mathematical expressions. Recently, deep learning has emerged as a powerful tool for extracting insights from data. However, existing models typically specialize in either numeric or symbolic domains, and are usually trained in a supervised manner tailored to specific tasks. This approach neglects the substantial benefits that could arise from a task-agnostic unified understanding between symbolic equations and their numeric counterparts. To bridge the gap, we introduce SNIP, a Symbolic-Numeric Integrated Pre-training, which employs joint contrastive learning between symbolic and numeric domains, enhancing their mutual similarities in the pre-trained embeddings. By performing latent space analysis, we observe that SNIP provides cross-domain insights into the representations, revealing that symbolic supervision enhances the embeddings of numeric data and vice versa. We evaluate SNIP across diverse tasks, including symbolic-to-numeric mathematical property prediction and numeric-to-symbolic equation discovery, commonly known as symbolic regression. Results show that SNIP effectively transfers to various tasks, consistently outperforming fully supervised baselines and competing strongly with established task-specific methods, especially in few-shot learning scenarios where available data is limited.

Bayesian optimization (BO) is widely used for black-box optimization problems, and have been shown to perform well in various real-world tasks. However, most of the existing BO methods aim to learn the optimal solution, which may become infeasible when the parameter space is extremely large or the problem is time-sensitive. In these contexts, switching to a satisficing solution that requires less information can result in better performance. In this work, we focus on time-sensitive black-box optimization problems and propose satisficing Thompson sampling-based parallel Bayesian optimization (STS-PBO) approaches, including synchronous and asynchronous versions. We shift the target from an optimal solution to a satisficing solution that is easier to learn. The rate-distortion theory is introduced to construct a loss function that balances the amount of information that needs to be learned with sub-optimality, and the Blahut-Arimoto algorithm is adopted to compute the target solution that reaches the minimum information rate under the distortion limit at each step. Both discounted and undiscounted Bayesian cumulative regret bounds are theoretically derived for the proposed STS-PBO approaches. The effectiveness of the proposed methods is demonstrated on a fast-charging design problem of Lithium-ion batteries. The results are accordant with theoretical analyses, and show that our STS-PBO methods outperform both sequential counterparts and parallel BO with traditional Thompson sampling in both synchronous and asynchronous settings.

We commonly encounter the problem of identifying an optimally weight adjusted version of the empirical distribution of observed data, adhering to predefined constraints on the weights. Such constraints often manifest as restrictions on the moments, tail behaviour, shapes, number of modes, etc., of the resulting weight adjusted empirical distribution. In this article, we substantially enhance the flexibility of such methodology by introducing a nonparametrically imbued distributional constraints on the weights, and developing a general framework leveraging the maximum entropy principle and tools from optimal transport. The key idea is to ensure that the maximum entropy weight adjusted empirical distribution of the observed data is close to a pre-specified probability distribution in terms of the optimal transport metric while allowing for subtle departures. The versatility of the framework is demonstrated in the context of three disparate applications where data re-weighting is warranted to satisfy side constraints on the optimization problem at the heart of the statistical task: namely, portfolio allocation, semi-parametric inference for complex surveys, and ensuring algorithmic fairness in machine learning algorithms.

The deep learning architecture associated with ChatGPT and related generative AI products is known as transformers. Initially applied to Natural Language Processing, transformers and the self-attention mechanism they exploit have gained widespread interest across the natural sciences. The goal of this pedagogical and informal review is to introduce transformers to scientists. The review includes the mathematics underlying the attention mechanism, a description of the original transformer architecture, and a section on applications to time series and imaging data in astronomy. We include a Frequently Asked Questions section for readers who are curious about generative AI or interested in getting started with transformers for their research problem.

Protein structure-based property prediction has emerged as a promising approach for various biological tasks, such as protein function prediction and sub-cellular location estimation. The existing methods highly rely on experimental protein structure data and fail in scenarios where these data are unavailable. Predicted protein structures from AI tools (e.g., AlphaFold2) were utilized as alternatives. However, we observed that current practices, which simply employ accurately predicted structures during inference, suffer from notable degradation in prediction accuracy. While similar phenomena have been extensively studied in general fields (e.g., Computer Vision) as model robustness, their impact on protein property prediction remains unexplored. In this paper, we first investigate the reason behind the performance decrease when utilizing predicted structures, attributing it to the structure embedding bias from the perspective of structure representation learning. To study this problem, we identify a Protein 3D Graph Structure Learning Problem for Robust Protein Property Prediction (PGSL-RP3), collect benchmark datasets, and present a protein Structure embedding Alignment Optimization framework (SAO) to mitigate the problem of structure embedding bias between the predicted and experimental protein structures. Extensive experiments have shown that our framework is model-agnostic and effective in improving the property prediction of both predicted structures and experimental structures. The benchmark datasets and codes will be released to benefit the community.

Training a large and state-of-the-art machine learning model typically necessitates the use of large-scale datasets, which, in turn, makes the training and parameter-tuning process expensive and time-consuming. Some researchers opt to distil information from real-world datasets into tiny and compact synthetic datasets while maintaining their ability to train a well-performing model, hence proposing a data-efficient method known as Dataset Distillation (DD). Despite recent progress in this field, existing methods still underperform and cannot effectively replace large datasets. In this paper, unlike previous methods that focus solely on improving the efficacy of student distillation, we are the first to recognize the important interplay between expert and student. We argue the significant impact of expert smoothness when employing more potent expert trajectories in subsequent dataset distillation. Based on this, we introduce the integration of clipping loss and gradient penalty to regulate the rate of parameter changes in expert trajectories. Furthermore, in response to the sensitivity exhibited towards randomly initialized variables during distillation, we propose representative initialization for synthetic dataset and balanced inner-loop loss. Finally, we present two enhancement strategies, namely intermediate matching loss and weight perturbation, to mitigate the potential occurrence of cumulative errors. We conduct extensive experiments on datasets of different scales, sizes, and resolutions. The results demonstrate that the proposed method significantly outperforms prior methods.

Feature preprocessing continues to play a critical role when applying machine learning and statistical methods to tabular data. In this paper, we propose the use of the kernel density integral transformation as a feature preprocessing step. Our approach subsumes the two leading feature preprocessing methods as limiting cases: linear min-max scaling and quantile transformation. We demonstrate that, without hyperparameter tuning, the kernel density integral transformation can be used as a simple drop-in replacement for either method, offering protection from the weaknesses of each. Alternatively, with tuning of a single continuous hyperparameter, we frequently outperform both of these methods. Finally, we show that the kernel density transformation can be profitably applied to statistical data analysis, particularly in correlation analysis and univariate clustering.

Narrow bit-width data formats are key to reducing the computational and storage costs of modern deep learning applications. This paper evaluates Microscaling (MX) data formats that combine a per-block scaling factor with narrow floating-point and integer types for individual elements. MX formats balance the competing needs of hardware efficiency, model accuracy, and user friction. Empirical results on over two dozen benchmarks demonstrate practicality of MX data formats as a drop-in replacement for baseline FP32 for AI inference and training with low user friction. We also show the first instance of training generative language models at sub-8-bit weights, activations, and gradients with minimal accuracy loss and no modifications to the training recipe.

The success of AI assistants based on language models (LLMs) hinges crucially on Reinforcement Learning from Human Feedback (RLHF), which enables the generation of responses more aligned with human preferences. As universal AI assistants, there's a growing expectation for them to perform consistently across various domains. However, previous work shows that Reinforcement Learning (RL) often exploits shortcuts to attain high rewards and overlooks challenging samples. This focus on quick reward gains undermines both the stability in training and the model's ability to generalize to new, unseen data. In this work, we propose a novel approach that can learn a consistent policy via RL across various data groups or domains. Given the challenges associated with acquiring group annotations, our method automatically classifies data into different groups, deliberately maximizing performance variance. Then, we optimize the policy to perform well on challenging groups. Lastly, leveraging the established groups, our approach adaptively adjusts the exploration space, allocating more learning capacity to more challenging data and preventing the model from over-optimizing on simpler data. Experimental results indicate that our approach significantly enhances training stability and model generalization.

Probabilistic hierarchical time-series forecasting is an important variant of time-series forecasting, where the goal is to model and forecast multivariate time-series that have underlying hierarchical relations. Most methods focus on point predictions and do not provide well-calibrated probabilistic forecasts distributions. Recent state-of-art probabilistic forecasting methods also impose hierarchical relations on point predictions and samples of distribution which does not account for coherency of forecast distributions. Previous works also silently assume that datasets are always consistent with given hierarchical relations and do not adapt to real-world datasets that show deviation from this assumption. We close both these gap and propose PROFHiT, which is a fully probabilistic hierarchical forecasting model that jointly models forecast distribution of entire hierarchy. PROFHiT uses a flexible probabilistic Bayesian approach and introduces a novel Distributional Coherency regularization to learn from hierarchical relations for entire forecast distribution that enables robust and calibrated forecasts as well as adapt to datasets of varying hierarchical consistency. On evaluating PROFHiT over wide range of datasets, we observed 41-88% better performance in accuracy and significantly better calibration. Due to modeling the coherency over full distribution, we observed that PROFHiT can robustly provide reliable forecasts even if up to 10% of input time-series data is missing where other methods' performance severely degrade by over 70%.

Data privacy and long-tailed distribution are the norms rather than the exception in many real-world tasks. This paper investigates a federated long-tailed learning (Fed-LT) task in which each client holds a locally heterogeneous dataset; if the datasets can be globally aggregated, they jointly exhibit a long-tailed distribution. Under such a setting, existing federated optimization and/or centralized long-tailed learning methods hardly apply due to challenges in (a) characterizing the global long-tailed distribution under privacy constraints and (b) adjusting the local learning strategy to cope with the head-tail imbalance. In response, we propose a method termed $\texttt{Fed-GraB}$, comprised of a Self-adjusting Gradient Balancer (SGB) module that re-weights clients' gradients in a closed-loop manner, based on the feedback of global long-tailed distribution evaluated by a Direct Prior Analyzer (DPA) module. Using $\texttt{Fed-GraB}$, clients can effectively alleviate the distribution drift caused by data heterogeneity during the model training process and obtain a global model with better performance on the minority classes while maintaining the performance of the majority classes. Extensive experiments demonstrate that $\texttt{Fed-GraB}$ achieves state-of-the-art performance on representative datasets such as CIFAR-10-LT, CIFAR-100-LT, ImageNet-LT, and iNaturalist.

Finding and selecting new and interesting problems to solve is at the heart of curiosity, science and innovation. We here study automated problem generation in the context of the open-ended space of python programming puzzles. Existing generative models often aim at modeling a reference distribution without any explicit diversity optimization. Other methods explicitly optimizing for diversity do so either in limited hand-coded representation spaces or in uninterpretable learned embedding spaces that may not align with human perceptions of interesting variations. With ACES (Autotelic Code Exploration via Semantic descriptors), we introduce a new autotelic generation method that leverages semantic descriptors produced by a large language model (LLM) to directly optimize for interesting diversity, as well as few-shot-based generation. Each puzzle is labeled along 10 dimensions, each capturing a programming skill required to solve it. ACES generates and pursues novel and feasible goals to explore that abstract semantic space, slowly discovering a diversity of solvable programming puzzles in any given run. Across a set of experiments, we show that ACES discovers a richer diversity of puzzles than existing diversity-maximizing algorithms as measured across a range of diversity metrics. We further study whether and in which conditions this diversity can translate into the successful training of puzzle solving models.

Representation learning has significantly driven the field to develop pretrained models that can act as a valuable starting point when transferring to new datasets. With the rising demand for reliable machine learning and uncertainty quantification, there is a need for pretrained models that not only provide embeddings but also transferable uncertainty estimates. To guide the development of such models, we propose the Uncertainty-aware Representation Learning (URL) benchmark. Besides the transferability of the representations, it also measures the zero-shot transferability of the uncertainty estimate using a novel metric. We apply URL to evaluate eleven uncertainty quantifiers that are pretrained on ImageNet and transferred to eight downstream datasets. We find that approaches that focus on the uncertainty of the representation itself or estimate the prediction risk directly outperform those that are based on the probabilities of upstream classes. Yet, achieving transferable uncertainty quantification remains an open challenge. Our findings indicate that it is not necessarily in conflict with traditional representation learning goals. Code is provided under https://github.com/mkirchhof/url .

In this article we present new results about the expressivity of Graph Neural Networks (GNNs). We prove that for any GNN with piecewise polynomial activations, whose architecture size does not grow with the graph input sizes, there exists a pair of non-isomorphic rooted trees of depth two such that the GNN cannot distinguish their root vertex up to an arbitrary number of iterations. The proof relies on tools from the algebra of symmetric polynomials. In contrast, it was already known that unbounded GNNs (those whose size is allowed to change with the graph sizes) with piecewise polynomial activations can distinguish these vertices in only two iterations. Our results imply a strict separation between bounded and unbounded size GNNs, answering an open question formulated by [Grohe, 2021]. We next prove that if one allows activations that are not piecewise polynomial, then in two iterations a single neuron perceptron can distinguish the root vertices of any pair of nonisomorphic trees of depth two (our results hold for activations like the sigmoid, hyperbolic tan and others). This shows how the power of graph neural networks can change drastically if one changes the activation function of the neural networks. The proof of this result utilizes the Lindemann-Weierstrauss theorem from transcendental number theory.

Large language models (LMs) are currently trained to predict tokens given document prefixes, enabling them to directly perform long-form generation and prompting-style tasks which can be reduced to document completion. Existing pretraining pipelines train LMs by concatenating random sets of short documents to create input contexts but the prior documents provide no signal for predicting the next document. We instead present In-Context Pretraining, a new approach where language models are pretrained on a sequence of related documents, thereby explicitly encouraging them to read and reason across document boundaries. We can do In-Context Pretraining by simply changing the document ordering so that each context contains related documents, and directly applying existing pretraining pipelines. However, this document sorting problem is challenging. There are billions of documents and we would like the sort to maximize contextual similarity for every document without repeating any data. To do this, we introduce approximate algorithms for finding related documents with efficient nearest neighbor search and constructing coherent input contexts with a graph traversal algorithm. Our experiments show In-Context Pretraining offers a simple and scalable approach to significantly enhance LMs'performance: we see notable improvements in tasks that require more complex contextual reasoning, including in-context learning (+8%), reading comprehension (+15%), faithfulness to previous contexts (+16%), long-context reasoning (+5%), and retrieval augmentation (+9%).

Generative self-supervised learning (SSL) has exhibited significant potential and garnered increasing interest in graph learning. In this study, we aim to explore the problem of generative SSL in the context of heterogeneous graph learning (HGL). The previous SSL approaches for heterogeneous graphs have primarily relied on contrastive learning, necessitating the design of complex views to capture heterogeneity. However, existing generative SSL methods have not fully leveraged the capabilities of generative models to address the challenges of HGL. In this paper, we present HGCVAE, a novel contrastive variational graph auto-encoder that liberates HGL from the burden of intricate heterogeneity capturing. Instead of focusing on complicated heterogeneity, HGCVAE harnesses the full potential of generative SSL. HGCVAE innovatively consolidates contrastive learning with generative SSL, introducing several key innovations. Firstly, we employ a progressive mechanism to generate high-quality hard negative samples for contrastive learning, utilizing the power of variational inference. Additionally, we present a dynamic mask strategy to ensure effective and stable learning. Moreover, we propose an enhanced scaled cosine error as the criterion for better attribute reconstruction. As an initial step in combining generative and contrastive SSL, HGCVAE achieves remarkable results compared to various state-of-the-art baselines, confirming its superiority.

Movement primitives are trainable parametric models that reproduce robotic movements starting from a limited set of demonstrations. Previous works proposed simple linear models that exhibited high sample efficiency and generalization power by allowing temporal modulation of movements (reproducing movements faster or slower), blending (merging two movements into one), via-point conditioning (constraining a movement to meet some particular via-points) and context conditioning (generation of movements based on an observed variable, e.g., position of an object). Previous works have proposed neural network-based motor primitive models, having demonstrated their capacity to perform tasks with some forms of input conditioning or time-modulation representations. However, there has not been a single unified deep motor primitive's model proposed that is capable of all previous operations, limiting neural motor primitive's potential applications. This paper proposes a deep movement primitive architecture that encodes all the operations above and uses a Bayesian context aggregator that allows a more sound context conditioning and blending. Our results demonstrate our approach can scale to reproduce complex motions on a larger variety of input choices compared to baselines while maintaining operations of linear movement primitives provide.

The mixing of two or more languages is called Code-Mixing (CM). CM is a social norm in multilingual societies. Neural Language Models (NLMs) like transformers have been effective on many NLP tasks. However, NLM for CM is an under-explored area. Though transformers are capable and powerful, they cannot always encode positional information since they are non-recurrent. Therefore, to enrich word information and incorporate positional information, positional encoding is defined. We hypothesize that Switching Points (SPs), i.e., junctions in the text where the language switches (L1 -> L2 or L2 -> L1), pose a challenge for CM Language Models (LMs), and hence give special emphasis to SPs in the modeling process. We experiment with several positional encoding mechanisms and show that rotatory positional encodings along with switching point information yield the best results. We introduce CONFLATOR: a neural language modeling approach for code-mixed languages. CONFLATOR tries to learn to emphasize switching points using smarter positional encoding, both at unigram and bigram levels. CONFLATOR outperforms the state-of-the-art on two tasks based on code-mixed Hindi and English (Hinglish): (i) sentiment analysis and (ii) machine translation.

If machine learning models were to achieve superhuman abilities at various reasoning or decision-making tasks, how would we go about evaluating such models, given that humans would necessarily be poor proxies for ground truth? In this paper, we propose a framework for evaluating superhuman models via consistency checks. Our premise is that while the correctness of superhuman decisions may be impossible to evaluate, we can still surface mistakes if the model's decisions fail to satisfy certain logical, human-interpretable rules. We instantiate our framework on three tasks where correctness of decisions is hard to evaluate due to either superhuman model abilities, or to otherwise missing ground truth: evaluating chess positions, forecasting future events, and making legal judgments. We show that regardless of a model's (possibly superhuman) performance on these tasks, we can discover logical inconsistencies in decision making. For example: a chess engine assigning opposing valuations to semantically identical boards; GPT-4 forecasting that sports records will evolve non-monotonically over time; or an AI judge assigning bail to a defendant only after we add a felony to their criminal record.

Neural networks have achieved remarkable empirical performance, while the current theoretical analysis is not adequate for understanding their success, e.g., the Neural Tangent Kernel approach fails to capture their key feature learning ability, while recent analyses on feature learning are typically problem-specific. This work proposes a unified analysis framework for two-layer networks trained by gradient descent. The framework is centered around the principle of feature learning from gradients, and its effectiveness is demonstrated by applications in several prototypical problems, such as mixtures of Gaussians and parity functions. The framework also sheds light on interesting network learning phenomena such as feature learning beyond kernels and the lottery ticket hypothesis.

Supervised contrastive loss (SCL) is a competitive and often superior alternative to the cross-entropy loss for classification. While prior studies have demonstrated that both losses yield symmetric training representations under balanced data, this symmetry breaks under class imbalances. This paper presents an intriguing discovery: the introduction of a ReLU activation at the final layer effectively restores the symmetry in SCL-learned representations. We arrive at this finding analytically, by establishing that the global minimizers of an unconstrained features model with SCL loss and entry-wise non-negativity constraints form an orthogonal frame. Extensive experiments conducted across various datasets, architectures, and imbalance scenarios corroborate our finding. Importantly, our experiments reveal that the inclusion of the ReLU activation restores symmetry without compromising test accuracy. This constitutes the first geometry characterization of SCL under imbalances. Additionally, our analysis and experiments underscore the pivotal role of batch selection strategies in representation geometry. By proving necessary and sufficient conditions for mini-batch choices that ensure invariant symmetric representations, we introduce batch-binding as an efficient strategy that guarantees these conditions hold.

Large Language Models (LLMs) have shown impressive performance as general purpose agents, but their abilities remain highly dependent on prompts which are hand written with onerous trial-and-error effort. We propose a simple and nonparametric solution to this problem, Automatic Prompt Optimization (APO), which is inspired by numerical gradient descent to automatically improve prompts, assuming access to training data and an LLM API. The algorithm uses minibatches of data to form natural language "gradients" that criticize the current prompt. The gradients are then "propagated" into the prompt by editing the prompt in the opposite semantic direction of the gradient. These gradient descent steps are guided by a beam search and bandit selection procedure which significantly improves algorithmic efficiency. Preliminary results across three benchmark NLP tasks and the novel problem of LLM jailbreak detection suggest that Automatic Prompt Optimization can outperform prior prompt editing techniques and improve an initial prompt's performance by up to 31%, by using data to rewrite vague task descriptions into more precise annotation instructions.

Diffusion models are generative models, which gradually add and remove noise to learn the underlying distribution of training data for data generation. The components of diffusion models have gained significant attention with many design choices proposed. Existing reviews have primarily focused on higher-level solutions, thereby covering less on the design fundamentals of components. This study seeks to address this gap by providing a comprehensive and coherent review on component-wise design choices in diffusion models. Specifically, we organize this review according to their three key components, namely the forward process, the reverse process, and the sampling procedure. This allows us to provide a fine-grained perspective of diffusion models, benefiting future studies in the analysis of individual components, the applicability of design choices, and the implementation of diffusion models.

Parameter-efficient fine-tuning (PEFT) of pre-trained language models (PLMs) has emerged as a highly successful approach, with training only a small number of parameters without sacrificing performance and becoming the de-facto learning paradigm with the increasing size of PLMs. However, existing PEFT methods are not memory-efficient, because they still require caching most of the intermediate activations for the gradient calculation, akin to fine-tuning. One effective way to reduce the activation memory is to apply a reversible model, so the intermediate activations are not necessary to be cached and can be recomputed. Nevertheless, modifying a PLM to its reversible variant is not straightforward, since the reversible model has a distinct architecture from the currently released PLMs. In this paper, we first investigate what is a key factor for the success of existing PEFT methods, and realize that it's essential to preserve the PLM's starting point when initializing a PEFT method. With this finding, we propose memory-efficient fine-tuning (MEFT) that inserts adapters into a PLM, preserving the PLM's starting point and making it reversible without additional pre-training. We evaluate MEFT on the GLUE benchmark and five question-answering tasks with various backbones, BERT, RoBERTa, BART and OPT. MEFT significantly reduces the activation memory up to 84% of full fine-tuning with a negligible amount of trainable parameters. Moreover, MEFT achieves the same score on GLUE and a comparable score on the question-answering tasks as full fine-tuning. A similar finding is also observed for the image classification task.

As generative AI becomes more prevalent, it is important to study how human users interact with such models. In this work, we investigate how people use text-to-image models to generate desired target images. To study this interaction, we created ArtWhisperer, an online game where users are given a target image and are tasked with iteratively finding a prompt that creates a similar-looking image as the target. Through this game, we recorded over 50,000 human-AI interactions; each interaction corresponds to one text prompt created by a user and the corresponding generated image. The majority of these are repeated interactions where a user iterates to find the best prompt for their target image, making this a unique sequential dataset for studying human-AI collaborations. In an initial analysis of this dataset, we identify several characteristics of prompt interactions and user strategies. People submit diverse prompts and are able to discover a variety of text descriptions that generate similar images. Interestingly, prompt diversity does not decrease as users find better prompts. We further propose a new metric to quantify the steerability of AI using our dataset. We define steerability as the expected number of interactions required to adequately complete a task. We estimate this value by fitting a Markov chain for each target task and calculating the expected time to reach an adequate score in the Markov chain. We quantify and compare AI steerability across different types of target images and two different models, finding that images of cities and natural world images are more steerable than artistic and fantasy images. These findings provide insights into human-AI interaction behavior, present a concrete method of assessing AI steerability, and demonstrate the general utility of the ArtWhisperer dataset.

The Quasi Manhattan Wasserstein Distance (QMWD) is a metric designed to quantify the dissimilarity between two matrices by combining elements of the Wasserstein Distance with specific transformations. It offers improved time and space complexity compared to the Manhattan Wasserstein Distance (MWD) while maintaining accuracy. QMWD is particularly advantageous for large datasets or situations with limited computational resources. This article provides a detailed explanation of QMWD, its computation, complexity analysis, and comparisons with WD and MWD.

This paper proposes the use of causal modeling to detect and mitigate algorithmic bias. We provide a brief description of causal modeling and a general overview of our approach. We then use the Adult dataset, which is available for download from the UC Irvine Machine Learning Repository, to develop (1) a prediction model, which is treated as a black box, and (2) a causal model for bias mitigation. In this paper, we focus on gender bias and the problem of binary classification. We show that gender bias in the prediction model is statistically significant at the 0.05 level. We demonstrate the effectiveness of the causal model in mitigating gender bias by cross-validation. Furthermore, we show that the overall classification accuracy is improved slightly. Our novel approach is intuitive, easy-to-use, and can be implemented using existing statistical software tools such as "lavaan" in R. Hence, it enhances explainability and promotes trust.

This paper studies a long-term resource allocation problem over multiple periods where each period requires a multi-stage decision-making process. We formulate the problem as an online allocation problem in an episodic finite-horizon constrained Markov decision process with an unknown non-stationary transition function and stochastic non-stationary reward and resource consumption functions. We propose the observe-then-decide regime and improve the existing decide-then-observe regime, while the two settings differ in how the observations and feedback about the reward and resource consumption functions are given to the decision-maker. We develop an online dual mirror descent algorithm that achieves near-optimal regret bounds for both settings. For the observe-then-decide regime, we prove that the expected regret against the dynamic clairvoyant optimal policy is bounded by $\tilde O(\rho^{-1}{H^{3/2}}S\sqrt{AT})$ where $\rho\in(0,1)$ is the budget parameter, $H$ is the length of the horizon, $S$ and $A$ are the numbers of states and actions, and $T$ is the number of episodes. For the decide-then-observe regime, we show that the regret against the static optimal policy that has access to the mean reward and mean resource consumption functions is bounded by $\tilde O(\rho^{-1}{H^{3/2}}S\sqrt{AT})$ with high probability. We test the numerical efficiency of our method for a variant of the resource-constrained inventory management problem.

Most existing parametric query optimization (PQO) techniques rely on traditional query optimizer cost models, which are often inaccurate and result in suboptimal query performance. We propose Kepler, an end-to-end learning-based approach to PQO that demonstrates significant speedups in query latency over a traditional query optimizer. Central to our method is Row Count Evolution (RCE), a novel plan generation algorithm based on perturbations in the sub-plan cardinality space. While previous approaches require accurate cost models, we bypass this requirement by evaluating candidate plans via actual execution data and training an ML model to predict the fastest plan given parameter binding values. Our models leverage recent advances in neural network uncertainty in order to robustly predict faster plans while avoiding regressions in query performance. Experimentally, we show that Kepler achieves significant improvements in query runtime on multiple datasets on PostgreSQL.

Multi-modal Contrastive Representation learning aims to encode different modalities into a semantically aligned shared space. This paradigm shows remarkable generalization ability on numerous downstream tasks across various modalities. However, the reliance on massive high-quality data pairs limits its further development on more modalities. This paper proposes a novel training-efficient method for learning MCR without paired data called Connecting Multi-modal Contrastive Representations (C-MCR). Specifically, given two existing MCRs pre-trained on (A, B) and (B, C) modality pairs, we project them to a new space and use the data from the overlapping modality B to aligning the two MCRs in the new space. Meanwhile, since the modality pairs (A, B) and (B, C) are already aligned within each MCR, the connection learned by overlapping modality can also be transferred to non-overlapping modality pair (A, C). To unleash the potential of C-MCR, we further introduce a semantic-enhanced inter- and intra-MCR connection method. We first enhance the semantic consistency and completion of embeddings across different modalities for more robust alignment. Then we utilize the inter-MCR alignment to establish the connection, and employ the intra-MCR alignment to better maintain the connection for inputs from non-overlapping modalities. To demonstrate the effectiveness of C-MCR, we connect CLIP and CLAP via texts to derive audio-visual representations, and integrate CLIP and ULIP via images for 3D-language representations. Remarkably, without using any paired data, C-MCR for audio-visual achieves state-of-the-art performance on audio-image retrieval, audio-visual source localization, and counterfactual audio-image recognition tasks. Furthermore, C-MCR for 3D-language also attains advanced zero-shot 3D point cloud classification accuracy on ModelNet40.

Multifunctional biological neural networks exploit multistability in order to perform multiple tasks without changing any network properties. Enabling artificial neural networks (ANNs) to obtain certain multistabilities in order to perform several tasks, where each task is related to a particular attractor in the network's state space, naturally has many benefits from a machine learning perspective. Given the association to multistability, in this paper we explore how the relationship between different attractors influences the ability of a reservoir computer (RC), which is a dynamical system in the form of an ANN, to achieve multifunctionality. We construct the `seeing double' problem to systematically study how a RC reconstructs a coexistence of attractors when there is an overlap between them. As the amount of overlap increases, we discover that for multifunctionality to occur, there is a critical dependence on a suitable choice of the spectral radius for the RC's internal network connections. A bifurcation analysis reveals how multifunctionality emerges and is destroyed as the RC enters a chaotic regime that can lead to chaotic itinerancy.

This paper introduces a new regularized version of the robust $\tau$-regression estimator for analyzing high-dimensional datasets subject to gross contamination in the response variables and covariates (explanatory variables). The resulting estimator, termed adaptive $\tau$-Lasso, is robust to outliers and high-leverage points. It also incorporates an adaptive $\ell_1$-norm penalty term, which enables the selection of relevant variables and reduces the bias associated with large true regression coefficients. More specifically, this adaptive $\ell_1$-norm penalty term assigns a weight to each regression coefficient. For a fixed number of predictors $p$, we show that the adaptive $\tau$-Lasso has the oracle property, ensuring both variable-selection consistency and asymptotic normality. Asymptotic normality applies only to the entries of the regression vector corresponding to the true support, assuming knowledge of the true regression vector support. We characterize its robustness via the finite-sample breakdown point and the influence function. We carry out extensive simulations and observe that the class of $\tau$-Lasso estimators exhibits robustness and reliable performance in both contaminated and uncontaminated data settings. We also validate our theoretical findings on robustness properties through simulation experiments. In the face of outliers and high-leverage points, the adaptive $\tau$-Lasso and $\tau$-Lasso estimators achieve the best performance or close-to-best performance in terms of prediction and variable selection accuracy compared to other competing regularized estimators for all scenarios considered in this study. Therefore, the adaptive $\tau$-Lasso and $\tau$-Lasso estimators can be effectively employed for a variety of sparse linear regression problems, particularly in high-dimensional settings and when the data is contaminated by outliers and high-leverage points.

Language-Conditioned Robotic Grasping (LCRG) aims to develop robots that ground and grasp objects based on natural language instructions. While robots capable of recognizing personal objects like "my wallet" can interact more naturally with non-expert users, current LCRG systems primarily limit robots to understanding only generic expressions. To this end, we introduce a task scenario GraspMine with a novel dataset that aims to locate and grasp personal objects given personal indicators via learning from a single human-robot interaction. To address GraspMine, we propose Personalized Grasping Agent (PGA), that learns personal objects by propagating user-given information through a Reminiscence-a collection of raw images from the user's environment. Specifically, PGA acquires personal object information by a user presenting a personal object with its associated indicator, followed by PGA inspecting the object by rotating it. Based on the acquired information, PGA pseudo-labels objects in the Reminiscence by our proposed label propagation algorithm. Harnessing the information acquired from the interactions and the pseudo-labeled objects in the Reminiscence, PGA adapts the object grounding model to grasp personal objects. Experiments on GraspMine show that PGA significantly outperforms baseline methods both in offline and online settings, signifying its effectiveness and personalization applicability on real-world scenarios. Finally, qualitative analysis shows the effectiveness of PGA through a detailed investigation of results in each phase.

We propose in this paper, STANLEY, a STochastic gradient ANisotropic LangEvin dYnamics, for sampling high dimensional data. With the growing efficacy and potential of Energy-Based modeling, also known as non-normalized probabilistic modeling, for modeling a generative process of different natures of high dimensional data observations, we present an end-to-end learning algorithm for Energy-Based models (EBM) with the purpose of improving the quality of the resulting sampled data points. While the unknown normalizing constant of EBMs makes the training procedure intractable, resorting to Markov Chain Monte Carlo (MCMC) is in general a viable option. Realizing what MCMC entails for the EBM training, we propose in this paper, a novel high dimensional sampling method, based on an anisotropic stepsize and a gradient-informed covariance matrix, embedded into a discretized Langevin diffusion. We motivate the necessity for an anisotropic update of the negative samples in the Markov Chain by the nonlinearity of the backbone of the EBM, here a Convolutional Neural Network. Our resulting method, namely STANLEY, is an optimization algorithm for training Energy-Based models via our newly introduced MCMC method. We provide a theoretical understanding of our sampling scheme by proving that the sampler leads to a geometrically uniformly ergodic Markov Chain. Several image generation experiments are provided in our paper to show the effectiveness of our method.

Privacy estimation techniques for differentially private (DP) algorithms are useful for comparing against analytical bounds, or to empirically measure privacy loss in settings where known analytical bounds are not tight. However, existing privacy auditing techniques usually make strong assumptions on the adversary (e.g., knowledge of intermediate model iterates or the training data distribution), are tailored to specific tasks, model architectures, or DP algorithm, and/or require retraining the model many times (typically on the order of thousands). These shortcomings make deploying such techniques at scale difficult in practice, especially in federated settings where model training can take days or weeks. In this work, we present a novel ``one-shot'' approach that can systematically address these challenges, allowing efficient auditing or estimation of the privacy loss of a model during the same, single training run used to fit model parameters, and without requiring any a priori knowledge about the model architecture, task, or DP training algorithm. We show that our method provides provably correct estimates for the privacy loss under the Gaussian mechanism, and we demonstrate its performance on well-established FL benchmark datasets under several adversarial threat models.

A recommender system that optimizes its recommendations solely to fit a user's history of ratings for consumed items can create a filter bubble, wherein the user does not get to experience items from novel, unseen categories. One approach to mitigate this undesired behavior is to recommend items with high potential for serendipity, namely surprising items that are likely to be highly rated. In this paper, we propose a content-based formulation of serendipity that is rooted in Bayesian surprise and use it to measure the serendipity of items after they are consumed and rated by the user. When coupled with a collaborative-filtering component that identifies similar users, this enables recommending items with high potential for serendipity. To facilitate the evaluation of topic-level models for surprise and serendipity, we introduce a dataset of book reading histories extracted from Goodreads, containing over 26 thousand users and close to 1.3 million books, where we manually annotate 449 books read by 4 users in terms of their time-dependent, topic-level surprise. Experimental evaluations show that models that use Bayesian surprise correlate much better with the manual annotations of topic-level surprise than distance-based heuristics, and also obtain better serendipitous item recommendation performance.

We report a flexible language-model based deep learning strategy, applied here to solve complex forward and inverse problems in protein modeling, based on an attention neural network that integrates transformer and graph convolutional architectures in a causal multi-headed graph mechanism, to realize a generative pretrained model. The model is applied to predict secondary structure content (per-residue level and overall content), protein solubility, and sequencing tasks. Further trained on inverse tasks, the model is rendered capable of designing proteins with these properties as target features. The model is formulated as a general framework, completely prompt-based, and can be adapted for a variety of downstream tasks. We find that adding additional tasks yields emergent synergies that the model exploits in improving overall performance, beyond what would be possible by training a model on each dataset alone. Case studies are presented to validate the method, yielding protein designs specifically focused on structural proteins, but also exploring the applicability in the design of soluble, antimicrobial biomaterials. While our model is trained to ultimately perform 8 distinct tasks, with available datasets it can be extended to solve additional problems. In a broader sense, this work illustrates a form of multiscale modeling that relates a set of ultimate building blocks (here, byte-level utf8 characters that define the nature of the physical system at hand) to complex output. This materiomic scheme captures complex emergent relationships between universal building block and resulting properties via a synergizing learning capacity to express a set of potentialities embedded in the knowledge used in training, via the interplay of universality and diversity.

Web-scale search systems learn an encoder to embed a given query which is then hooked into an approximate nearest neighbor search (ANNS) pipeline to retrieve similar data points. To accurately capture tail queries and data points, learned representations typically are rigid, high-dimensional vectors that are generally used as-is in the entire ANNS pipeline and can lead to computationally expensive retrieval. In this paper, we argue that instead of rigid representations, different stages of ANNS can leverage adaptive representations of varying capacities to achieve significantly better accuracy-compute trade-offs, i.e., stages of ANNS that can get away with more approximate computation should use a lower-capacity representation of the same data point. To this end, we introduce AdANNS, a novel ANNS design framework that explicitly leverages the flexibility of Matryoshka Representations. We demonstrate state-of-the-art accuracy-compute trade-offs using novel AdANNS-based key ANNS building blocks like search data structures (AdANNS-IVF) and quantization (AdANNS-OPQ). For example on ImageNet retrieval, AdANNS-IVF is up to 1.5% more accurate than the rigid representations-based IVF at the same compute budget; and matches accuracy while being up to 90x faster in wall-clock time. For Natural Questions, 32-byte AdANNS-OPQ matches the accuracy of the 64-byte OPQ baseline constructed using rigid representations -- same accuracy at half the cost! We further show that the gains from AdANNS translate to modern-day composite ANNS indices that combine search structures and quantization. Finally, we demonstrate that AdANNS can enable inference-time adaptivity for compute-aware search on ANNS indices built non-adaptively on matryoshka representations. Code is open-sourced at https://github.com/RAIVNLab/AdANNS.

This work introduces the first toolkit around path-norms that is fully able to encompass general DAG ReLU networks with biases, skip connections and any operation based on the extraction of order statistics: max pooling, GroupSort etc. This toolkit notably allows us to establish generalization bounds for modern neural networks that are not only the most widely applicable path-norm based ones, but also recover or beat the sharpest known bounds of this type. These extended path-norms further enjoy the usual benefits of path-norms: ease of computation, invariance under the symmetries of the network, and improved sharpness on feedforward networks compared to the product of operators' norms, another complexity measure most commonly used. The versatility of the toolkit and its ease of implementation allow us to challenge the concrete promises of path-norm-based generalization bounds, by numerically evaluating the sharpest known bounds for ResNets on ImageNet.

The training process of ReLU neural networks often exhibits complicated nonlinear phenomena. The nonlinearity of models and non-convexity of loss pose significant challenges for theoretical analysis. Therefore, most previous theoretical works on the optimization dynamics of neural networks focus either on local analysis (like the end of training) or approximate linear models (like Neural Tangent Kernel). In this work, we conduct a complete theoretical characterization of the training process of a two-layer ReLU network trained by Gradient Flow on a linearly separable data. In this specific setting, our analysis captures the whole optimization process starting from random initialization to final convergence. Despite the relatively simple model and data that we studied, we reveal four different phases from the whole training process showing a general simplifying-to-complicating learning trend. Specific nonlinear behaviors can also be precisely identified and captured theoretically, such as initial condensation, saddle-to-plateau dynamics, plateau escape, changes of activation patterns, learning with increasing complexity, etc.

The transformer is a neural network component that can be used to learn useful representations of sequences or sets of data-points. The transformer has driven recent advances in natural language processing, computer vision, and spatio-temporal modelling. There are many introductions to transformers, but most do not contain precise mathematical descriptions of the architecture and the intuitions behind the design choices are often also missing. Moreover, as research takes a winding path, the explanations for the components of the transformer can be idiosyncratic. In this note we aim for a mathematically precise, intuitive, and clean description of the transformer architecture. We will not discuss training as this is rather standard. We assume that the reader is familiar with fundamental topics in machine learning including multi-layer perceptrons, linear transformations, softmax functions and basic probability.

Self-supervised Learning (SSL) including the mainstream contrastive learning has achieved great success in learning visual representations without data annotations. However, most methods mainly focus on the instance level information (\ie, the different augmented images of the same instance should have the same feature or cluster into the same class), but there is a lack of attention on the relationships between different instances. In this paper, we introduce a novel SSL paradigm, which we term as relational self-supervised learning (ReSSL) framework that learns representations by modeling the relationship between different instances. Specifically, our proposed method employs sharpened distribution of pairwise similarities among different instances as \textit{relation} metric, which is thus utilized to match the feature embeddings of different augmentations. To boost the performance, we argue that weak augmentations matter to represent a more reliable relation, and leverage momentum strategy for practical efficiency. The designed asymmetric predictor head and an InfoNCE warm-up strategy enhance the robustness to hyper-parameters and benefit the resulting performance. Experimental results show that our proposed ReSSL substantially outperforms the state-of-the-art methods across different network architectures, including various lightweight networks (\eg, EfficientNet and MobileNet).

Embodied agents operate in a structured world, often solving tasks with spatial, temporal, and permutation symmetries. Most algorithms for planning and model-based reinforcement learning (MBRL) do not take this rich geometric structure into account, leading to sample inefficiency and poor generalization. We introduce the Equivariant Diffuser for Generating Interactions (EDGI), an algorithm for MBRL and planning that is equivariant with respect to the product of the spatial symmetry group SE(3), the discrete-time translation group Z, and the object permutation group Sn. EDGI follows the Diffuser framework (Janner et al., 2022) in treating both learning a world model and planning in it as a conditional generative modeling problem, training a diffusion model on an offline trajectory dataset. We introduce a new SE(3)xZxSn-equivariant diffusion model that supports multiple representations. We integrate this model in a planning loop, where conditioning and classifier guidance let us softly break the symmetry for specific tasks as needed. On object manipulation and navigation tasks, EDGI is substantially more sample efficient and generalizes better across the symmetry group than non-equivariant models.

Data Augmentation (DA) has emerged as an indispensable strategy in Time Series Classification (TSC), primarily due to its capacity to amplify training samples, thereby bolstering model robustness, diversifying datasets, and curtailing overfitting. However, the current landscape of DA in TSC is plagued with fragmented literature reviews, nebulous methodological taxonomies, inadequate evaluative measures, and a dearth of accessible, user-oriented tools. In light of these challenges, this study embarks on an exhaustive dissection of DA methodologies within the TSC realm. Our initial approach involved an extensive literature review spanning a decade, revealing that contemporary surveys scarcely capture the breadth of advancements in DA for TSC, prompting us to meticulously analyze over 100 scholarly articles to distill more than 60 unique DA techniques. This rigorous analysis precipitated the formulation of a novel taxonomy, purpose-built for the intricacies of DA in TSC, categorizing techniques into five principal echelons: Transformation-Based, Pattern-Based, Generative, Decomposition-Based, and Automated Data Augmentation. Our taxonomy promises to serve as a robust navigational aid for scholars, offering clarity and direction in method selection. Addressing the conspicuous absence of holistic evaluations for prevalent DA techniques, we executed an all-encompassing empirical assessment, wherein upwards of 15 DA strategies were subjected to scrutiny across 8 UCR time-series datasets, employing ResNet and a multi-faceted evaluation paradigm encompassing Accuracy, Method Ranking, and Residual Analysis, yielding a benchmark accuracy of 88.94 +- 11.83%. Our investigation underscored the inconsistent efficacies of DA techniques, with...

Brain Signals, such as Electroencephalography (EEG), and human languages have been widely explored independently for many downstream tasks, however, the connection between them has not been well explored. In this study, we explore the relationship and dependency between EEG and language. To study at the representation level, we introduced \textbf{MTAM}, a \textbf{M}ultimodal \textbf{T}ransformer \textbf{A}lignment \textbf{M}odel, to observe coordinated representations between the two modalities. We used various relationship alignment-seeking techniques, such as Canonical Correlation Analysis and Wasserstein Distance, as loss functions to transfigure features. On downstream applications, sentiment analysis and relation detection, we achieved new state-of-the-art results on two datasets, ZuCo and K-EmoCon. Our method achieved an F1-score improvement of 1.7% on K-EmoCon and 9.3% on Zuco datasets for sentiment analysis, and 7.4% on ZuCo for relation detection. In addition, we provide interpretations of the performance improvement: (1) feature distribution shows the effectiveness of the alignment module for discovering and encoding the relationship between EEG and language; (2) alignment weights show the influence of different language semantics as well as EEG frequency features; (3) brain topographical maps provide an intuitive demonstration of the connectivity in the brain regions. Our code is available at \url{https://github.com/Jason-Qiu/EEG_Language_Alignment}.

Whether class labels in a given data set correspond to meaningful clusters is crucial for the evaluation of clustering algorithms using real-world data sets. This property can be quantified by separability measures. A review of the existing literature shows that neither classification-based complexity measures nor cluster validity indices (CVIs) adequately incorporate the central aspects of separability for density-based clustering: between-class separation and within-class connectedness. A newly developed measure (density cluster separability index, DCSI) aims to quantify these two characteristics and can also be used as a CVI. Extensive experiments on synthetic data indicate that DCSI correlates strongly with the performance of DBSCAN measured via the adjusted rand index (ARI) but lacks robustness when it comes to multi-class data sets with overlapping classes that are ill-suited for density-based hard clustering. Detailed evaluation on frequently used real-world data sets shows that DCSI can correctly identify touching or overlapping classes that do not form meaningful clusters.

Realistic and controllable traffic simulation is a core capability that is necessary to accelerate autonomous vehicle (AV) development. However, current approaches for controlling learning-based traffic models require significant domain expertise and are difficult for practitioners to use. To remedy this, we present CTG++, a scene-level conditional diffusion model that can be guided by language instructions. Developing this requires tackling two challenges: the need for a realistic and controllable traffic model backbone, and an effective method to interface with a traffic model using language. To address these challenges, we first propose a scene-level diffusion model equipped with a spatio-temporal transformer backbone, which generates realistic and controllable traffic. We then harness a large language model (LLM) to convert a user's query into a loss function, guiding the diffusion model towards query-compliant generation. Through comprehensive evaluation, we demonstrate the effectiveness of our proposed method in generating realistic, query-compliant traffic simulations.

Large-scale generative models such as GPT and DALL-E have revolutionized the research community. These models not only generate high fidelity outputs, but are also generalists which can solve tasks not explicitly taught. In contrast, speech generative models are still primitive in terms of scale and task generalization. In this paper, we present Voicebox, the most versatile text-guided generative model for speech at scale. Voicebox is a non-autoregressive flow-matching model trained to infill speech, given audio context and text, trained on over 50K hours of speech that are not filtered or enhanced. Similar to GPT, Voicebox can perform many different tasks through in-context learning, but is more flexible as it can also condition on future context. Voicebox can be used for mono or cross-lingual zero-shot text-to-speech synthesis, noise removal, content editing, style conversion, and diverse sample generation. In particular, Voicebox outperforms the state-of-the-art zero-shot TTS model VALL-E on both intelligibility (5.9% vs 1.9% word error rates) and audio similarity (0.580 vs 0.681) while being up to 20 times faster. Audio samples can be found in \url{https://voicebox.metademolab.com}.

Data-driven black-box model-based optimization (MBO) problems arise in a great number of practical application scenarios, where the goal is to find a design over the whole space maximizing a black-box target function based on a static offline dataset. In this work, we consider a more general but challenging MBO setting, named constrained MBO (CoMBO), where only part of the design space can be optimized while the rest is constrained by the environment. A new challenge arising from CoMBO is that most observed designs that satisfy the constraints are mediocre in evaluation. Therefore, we focus on optimizing these mediocre designs in the offline dataset while maintaining the given constraints rather than further boosting the best observed design in the traditional MBO setting. We propose retrieval-enhanced offline model-based optimization (ROMO), a new derivable forward approach that retrieves the offline dataset and aggregates relevant samples to provide a trusted prediction, and use it for gradient-based optimization. ROMO is simple to implement and outperforms state-of-the-art approaches in the CoMBO setting. Empirically, we conduct experiments on a synthetic Hartmann (3D) function dataset, an industrial CIO dataset, and a suite of modified tasks in the Design-Bench benchmark. Results show that ROMO performs well in a wide range of constrained optimization tasks.

Binary Neural Networks (BNNs) have emerged as a promising solution for reducing the memory footprint and compute costs of deep neural networks, but they suffer from quality degradation due to the lack of freedom as activations and weights are constrained to the binary values. To compensate for the accuracy drop, we propose a novel BNN design called Binary Neural Network with INSTAnce-aware threshold (INSTA-BNN), which controls the quantization threshold dynamically in an input-dependent or instance-aware manner. According to our observation, higher-order statistics can be a representative metric to estimate the characteristics of the input distribution. INSTA-BNN is designed to adjust the threshold dynamically considering various information, including higher-order statistics, but it is also optimized judiciously to realize minimal overhead on a real device. Our extensive study shows that INSTA-BNN outperforms the baseline by 3.0% and 2.8% on the ImageNet classification task with comparable computing cost, achieving 68.5% and 72.2% top-1 accuracy on ResNet-18 and MobileNetV1 based models, respectively.

Spectral clustering and its extensions usually consist of two steps: (1) constructing a graph and computing the relaxed solution; (2) discretizing relaxed solutions. Although the former has been extensively investigated, the discretization techniques are mainly heuristic methods, e.g., k-means, spectral rotation. Unfortunately, the goal of the existing methods is not to find a discrete solution that minimizes the original objective. In other words, the primary drawback is the neglect of the original objective when computing the discrete solution. Inspired by the first-order optimization algorithms, we propose to develop a first-order term to bridge the original problem and discretization algorithm, which is the first non-heuristic to the best of our knowledge. Since the non-heuristic method is aware of the original graph cut problem, the final discrete solution is more reliable and achieves the preferable loss value. We also theoretically show that the continuous optimum is beneficial to discretization algorithms though simply finding its closest discrete solution is an existing heuristic algorithm which is also unreliable. Sufficient experiments significantly show the superiority of our method.

Streaming submodular maximization is a natural model for the task of selecting a representative subset from a large-scale dataset. If datapoints have sensitive attributes such as gender or race, it becomes important to enforce fairness to avoid bias and discrimination. This has spurred significant interest in developing fair machine learning algorithms. Recently, such algorithms have been developed for monotone submodular maximization under a cardinality constraint. In this paper, we study the natural generalization of this problem to a matroid constraint. We give streaming algorithms as well as impossibility results that provide trade-offs between efficiency, quality and fairness. We validate our findings empirically on a range of well-known real-world applications: exemplar-based clustering, movie recommendation, and maximum coverage in social networks.

Recent parameter-efficient finetuning (PEFT) techniques aim to improve over the considerable cost of fully finetuning large pretrained language models (PLM). As different PEFT techniques proliferate, it is becoming difficult to compare them, in particular in terms of (i) the structure and functionality they add to the PLM, (ii) the different types and degrees of efficiency improvements achieved, (iii) performance at different downstream tasks, and (iv) how differences in structure and functionality relate to efficiency and task performance. To facilitate such comparisons, this paper presents a reference architecture which standardises aspects shared by different PEFT techniques, while isolating differences to specific locations and interactions with the standard components. Through this process of standardising and isolating differences, a modular view of PEFT techniques emerges, supporting not only direct comparison of different techniques and their efficiency and task performance, but also systematic exploration of reusability and composability of the different types of finetuned modules. We demonstrate how the reference architecture can be applied to understand properties and relative advantages of PEFT techniques, hence to inform selection of techniques for specific tasks, and design choices for new PEFT techniques.

Solving complex planning problems has been a long-standing challenge in computer science. Learning-based subgoal search methods have shown promise in tackling these problems, but they often suffer from a lack of completeness guarantees, meaning that they may fail to find a solution even if one exists. In this paper, we propose an efficient approach to augment a subgoal search method to achieve completeness in discrete action spaces. Specifically, we augment the high-level search with low-level actions to execute a multi-level (hybrid) search, which we call complete subgoal search. This solution achieves the best of both worlds: the practical efficiency of high-level search and the completeness of low-level search. We apply the proposed search method to a recently proposed subgoal search algorithm and evaluate the algorithm trained on offline data on complex planning problems. We demonstrate that our complete subgoal search not only guarantees completeness but can even improve performance in terms of search expansions for instances that the high-level could solve without low-level augmentations. Our approach makes it possible to apply subgoal-level planning for systems where completeness is a critical requirement.

Probabilistic hierarchical time-series forecasting is an important variant of time-series forecasting, where the goal is to model and forecast multivariate time-series that have underlying hierarchical relations. Most methods focus on point predictions and do not provide well-calibrated probabilistic forecasts distributions. Recent state-of-art probabilistic forecasting methods also impose hierarchical relations on point predictions and samples of distribution which does not account for coherency of forecast distributions. Previous works also silently assume that datasets are always consistent with given hierarchical relations and do not adapt to real-world datasets that show deviation from this assumption. We close both these gap and propose PROFHiT, which is a fully probabilistic hierarchical forecasting model that jointly models forecast distribution of entire hierarchy. PROFHiT uses a flexible probabilistic Bayesian approach and introduces a novel Distributional Coherency regularization to learn from hierarchical relations for entire forecast distribution that enables robust and calibrated forecasts as well as adapt to datasets of varying hierarchical consistency. On evaluating PROFHiT over wide range of datasets, we observed 41-88% better performance in accuracy and significantly better calibration. Due to modeling the coherency over full distribution, we observed that PROFHiT can robustly provide reliable forecasts even if up to 10% of input time-series data is missing where other methods' performance severely degrade by over 70%.

Prompt-based learning has been an effective paradigm for large pretrained language models (LLM), enabling few-shot or even zero-shot learning. Black-box prompt search has received growing interest recently for its distinctive properties of gradient-free optimization, proven particularly useful and powerful for model-as-a-service usage. However, the discrete nature and the complexity of combinatorial optimization hinder the efficiency of modern black-box approaches. Despite extensive research on search algorithms, the crucial aspect of search space design and optimization has been largely overlooked. In this paper, we first conduct a sensitivity analysis by prompting LLM, revealing that only a small number of tokens exert a disproportionate amount of influence on LLM predictions. Leveraging this insight, we propose the Clustering and Pruning for Efficient Black-box Prompt Search (ClaPS), a simple black-box search method that first clusters and prunes the search space to focus exclusively on influential prompt tokens. By employing even simple search methods within the pruned search space, ClaPS achieves state-of-the-art performance across various tasks and LLMs, surpassing the performance of complex approaches while significantly reducing search costs. Our findings underscore the critical role of search space design and optimization in enhancing both the usefulness and the efficiency of black-box prompt-based learning.

Urban environments are intricate systems where the breakdown of critical infrastructure can impact both the economic and social well-being of communities. Electricity systems hold particular significance, as they are essential for other infrastructure, and disruptions can trigger widespread consequences. Typically, assessing electricity availability requires ground-level data, a challenge in conflict zones and regions with limited access. This study shows how satellite imagery, social media, and information extraction can monitor blackouts and their perceived causes. Night-time light data (in March 2019 for Caracas, Venezuela) is used to indicate blackout regions. Twitter data is used to determine sentiment and topic trends, while statistical analysis and topic modeling delved into public perceptions regarding blackout causes. The findings show an inverse relationship between nighttime light intensity. Tweets mentioning the Venezuelan President displayed heightened negativity and a greater prevalence of blame-related terms, suggesting a perception of government accountability for the outages.

The reliance of text classifiers on spurious correlations can lead to poor generalization at deployment, raising concerns about their use in safety-critical domains such as healthcare. In this work, we propose to use counterfactual data augmentation, guided by knowledge of the causal structure of the data, to simulate interventions on spurious features and to learn more robust text classifiers. We show that this strategy is appropriate in prediction problems where the label is spuriously correlated with an attribute. Under the assumptions of such problems, we discuss the favorable sample complexity of counterfactual data augmentation, compared to importance re-weighting. Pragmatically, we match examples using auxiliary data, based on diff-in-diff methodology, and use a large language model (LLM) to represent a conditional probability of text. Through extensive experimentation on learning caregiver-invariant predictors of clinical diagnoses from medical narratives and on semi-synthetic data, we demonstrate that our method for simulating interventions improves out-of-distribution (OOD) accuracy compared to baseline invariant learning algorithms.

In this paper, we explore audio-editing with non-rigid text edits. We show that the proposed editing pipeline is able to create audio edits that remain faithful to the input audio. We explore text prompts that perform addition, style transfer, and in-painting. We quantitatively and qualitatively show that the edits are able to obtain results which outperform Audio-LDM, a recently released text-prompted audio generation model. Qualitative inspection of the results points out that the edits given by our approach remain more faithful to the input audio in terms of keeping the original onsets and offsets of the audio events.

Diagnosing and cleaning data is a crucial step for building robust machine learning systems. However, identifying problems within large-scale datasets with real-world distributions is challenging due to the presence of complex issues such as label errors, under-representation, and outliers. In this paper, we propose a unified approach for identifying the problematic data by utilizing a largely ignored source of information: a relational structure of data in the feature-embedded space. To this end, we present scalable and effective algorithms for detecting label errors and outlier data based on the relational graph structure of data. We further introduce a visualization tool that provides contextual information of a data point in the feature-embedded space, serving as an effective tool for interactively diagnosing data. We evaluate the label error and outlier/out-of-distribution (OOD) detection performances of our approach on the large-scale image, speech, and language domain tasks, including ImageNet, ESC-50, and SST2. Our approach achieves state-of-the-art detection performance on all tasks considered and demonstrates its effectiveness in debugging large-scale real-world datasets across various domains. We release codes at https://github.com/snu-mllab/Neural-Relation-Graph.

The quality of explanations for the predictions of complex machine learning predictors is often measured using insertion and deletion metrics, which assess the faithfulness of the explanations, i.e., how correctly the explanations reflect the predictor's behavior. To improve the faithfulness, we propose insertion/deletion metric-aware explanation-based optimization (ID-ExpO), which optimizes differentiable predictors to improve both insertion and deletion scores of the explanations while keeping their predictive accuracy. Since the original insertion and deletion metrics are indifferentiable with respect to the explanations and directly unavailable for gradient-based optimization, we extend the metrics to be differentiable and use them to formalize insertion and deletion metric-based regularizers. The experimental results on image and tabular datasets show that the deep neural networks-based predictors fine-tuned using ID-ExpO enable popular post-hoc explainers to produce more faithful and easy-to-interpret explanations while keeping high predictive accuracy.

As Natural Language Processing (NLP) algorithms continually achieve new milestones, out-of-distribution generalization remains a significant challenge. This paper addresses the issue of multi-source adaptation for unfamiliar domains: We leverage labeled data from multiple source domains to generalize to unknown target domains at training. Our innovative framework employs example-based Hypernetwork adaptation: a T5 encoder-decoder initially generates a unique signature from an input example, embedding it within the source domains' semantic space. This signature is subsequently utilized by a Hypernetwork to generate the task classifier's weights. We evaluated our method across two tasks - sentiment classification and natural language inference - in 29 adaptation scenarios, where it outpaced established algorithms. In an advanced version, the signature also enriches the input example's representation. We also compare our finetuned architecture to few-shot GPT-3, demonstrating its effectiveness in essential use cases. To our knowledge, this marks the first application of Hypernetworks to the adaptation for unknown domains.

Recent advancements in large language models (LLMs) have demonstrated remarkable abilities in handling a variety of natural language processing (NLP) downstream tasks, even on mathematical tasks requiring multi-step reasoning. In this report, we introduce the KwaiYiiMath which enhances the mathematical reasoning abilities of KwaiYiiBase1, by applying Supervised Fine-Tuning (SFT) and Reinforced Learning from Human Feedback (RLHF), including on both English and Chinese mathematical tasks. Meanwhile, we also constructed a small-scale Chinese primary school mathematics test set (named KMath), consisting of 188 examples to evaluate the correctness of the problem-solving process generated by the models. Empirical studies demonstrate that KwaiYiiMath can achieve state-of-the-art (SOTA) performance on GSM8k, CMath, and KMath compared with the similar size models, respectively.

We study the global linear convergence of policy gradient (PG) methods for finite-horizon continuous-time exploratory linear-quadratic control (LQC) problems. The setting includes stochastic LQC problems with indefinite costs and allows additional entropy regularisers in the objective. We consider a continuous-time Gaussian policy whose mean is linear in the state variable and whose covariance is state-independent. Contrary to discrete-time problems, the cost is noncoercive in the policy and not all descent directions lead to bounded iterates. We propose geometry-aware gradient descents for the mean and covariance of the policy using the Fisher geometry and the Bures-Wasserstein geometry, respectively. The policy iterates are shown to satisfy an a-priori bound, and converge globally to the optimal policy with a linear rate. We further propose a novel PG method with discrete-time policies. The algorithm leverages the continuous-time analysis, and achieves a robust linear convergence across different action frequencies. A numerical experiment confirms the convergence and robustness of the proposed algorithm.

If you ask a human to describe an image, they might do so in a thousand different ways. Traditionally, image captioning models are trained to generate a single "best" (most like a reference) image caption. Unfortunately, doing so encourages captions that are "informationally impoverished," and focus on only a subset of the possible details, while ignoring other potentially useful information in the scene. In this work, we introduce a simple, yet novel, method: "Image Captioning by Committee Consensus" (IC3), designed to generate a single caption that captures high-level details from several annotator viewpoints. Humans rate captions produced by IC3 at least as helpful as baseline SOTA models more than two thirds of the time, and IC3 can improve the performance of SOTA automated recall systems by up to 84%, outperforming single human-generated reference captions, and indicating significant improvements over SOTA approaches for visual description. Code is available at https://davidmchan.github.io/caption-by-committee/

We study the compute-optimal trade-off between model and training data set sizes for large neural networks. Our result suggests a linear relation similar to that supported by the empirical analysis of chinchilla. While that work studies transformer-based large language models trained on the MassiveText corpus gopher, as a starting point for development of a mathematical theory, we focus on a simpler learning model and data generating process, each based on a neural network with a sigmoidal output unit and single hidden layer of ReLU activation units. We introduce general error upper bounds for a class of algorithms which incrementally update a statistic (for example gradient descent). For a particular learning model inspired by barron 1993, we establish an upper bound on the minimal information-theoretically achievable expected error as a function of model and data set sizes. We then derive allocations of computation that minimize this bound. We present empirical results which suggest that this approximation correctly identifies an asymptotic linear compute-optimal scaling. This approximation also generates new insights. Among other things, it suggests that, as the input dimension or latent space complexity grows, as might be the case for example if a longer history of tokens is taken as input to a language model, a larger fraction of the compute budget should be allocated to growing the learning model rather than training data.

We introduce DISSC, a novel, lightweight method that converts the rhythm, pitch contour and timbre of a recording to a target speaker in a textless manner. Unlike DISSC, most voice conversion (VC) methods focus primarily on timbre, and ignore people's unique speaking style (prosody). The proposed approach uses a pretrained, self-supervised model for encoding speech to discrete units, which makes it simple, effective, and fast to train. All conversion modules are only trained on reconstruction like tasks, thus suitable for any-to-many VC with no paired data. We introduce a suite of quantitative and qualitative evaluation metrics for this setup, and empirically demonstrate that DISSC significantly outperforms the evaluated baselines. Code and samples are available at https://pages.cs.huji.ac.il/adiyoss-lab/dissc/.

An important application scenario of precision agriculture is detecting and measuring crop health threats using sensors and data analysis techniques. However, the textual data are still under-explored among the existing solutions due to the lack of labelled data and fine-grained semantic resources. Recent research suggests that the increasing connectivity of farmers and the emergence of online farming communities make social media like Twitter a participatory platform for detecting unfamiliar plant health events if we can extract essential information from unstructured textual data. ChouBERT is a French pre-trained language model that can identify Tweets concerning observations of plant health issues with generalizability on unseen natural hazards. This paper tackles the lack of labelled data by further studying ChouBERT's know-how on token-level annotation tasks over small labeled sets.

In recent years, reinforcement learning and bandits have transformed a wide range of real-world applications including healthcare, finance, recommendation systems, robotics, and last but not least, the speech and natural language processing. While most speech and language applications of reinforcement learning algorithms are centered around improving the training of deep neural networks with its flexible optimization properties, there are still many grounds to explore to utilize the benefits of reinforcement learning, such as its reward-driven adaptability, state representations, temporal structures and generalizability. In this survey, we present an overview of recent advancements of reinforcement learning and bandits, and discuss how they can be effectively employed to solve speech and natural language processing problems with models that are adaptive, interactive and scalable.

Deep discriminative approaches like random forests and deep neural networks have recently found applications in many important real-world scenarios. However, deploying these learning algorithms in safety-critical applications raises concerns, particularly when it comes to ensuring confidence calibration for both in-distribution and out-of-distribution data points. Many popular methods for in-distribution (ID) calibration, such as isotonic regression and Platt's sigmoidal regression, exhibit excellent ID calibration performance but often at the cost of classification accuracy. Moreover, these methods are not calibrated for the entire feature space, leading to overconfidence in the case of out-of-distribution (OOD) samples. In this paper, we leveraged the fact that deep models, including both random forests and deep-nets, learn internal representations which are unions of polytopes with affine activation functions to conceptualize them both as partitioning rules of the feature space. We replace the affine function in each polytope populated by the training data with a Gaussian kernel. We propose sufficient conditions for our proposed methods to be consistent estimators of the corresponding class conditional densities. Moreover, our experiments on both tabular and vision benchmarks show that the proposed approaches obtain well-calibrated posteriors while mostly preserving or improving the classification accuracy of the original algorithm for in-distribution region, and extrapolates beyond the training data to handle out-of-distribution inputs appropriately.

Graph Neural Networks (GNNs) have shown state-of-the-art improvements in node classification tasks on graphs. While these improvements have been largely demonstrated in a multi-class classification scenario, a more general and realistic scenario in which each node could have multiple labels has so far received little attention. The first challenge in conducting focused studies on multi-label node classification is the limited number of publicly available multi-label graph datasets. Therefore, as our first contribution, we collect and release three real-world biological datasets and develop a multi-label graph generator to generate datasets with tunable properties. While high label similarity (high homophily) is usually attributed to the success of GNNs, we argue that a multi-label scenario does not follow the usual semantics of homophily and heterophily so far defined for a multi-class scenario. As our second contribution, we define homophily and Cross-Class Neighborhood Similarity for the multi-label scenario and provide a thorough analyses of the collected $9$ multi-label datasets. Finally, we perform a large-scale comparative study with $8$ methods and $9$ datasets and analyse the performances of the methods to assess the progress made by current state of the art in the multi-label node classification scenario. We release our benchmark at https://github.com/Tianqi-py/MLGNC.

When learning graph neural networks (GNNs) in node-level prediction tasks, most existing loss functions are applied for each node independently, even if node embeddings and their labels are non-i.i.d. because of their graph structures. To eliminate such inconsistency, in this study we propose a novel Quasi-Wasserstein (QW) loss with the help of the optimal transport defined on graphs, leading to new learning and prediction paradigms of GNNs. In particular, we design a "Quasi-Wasserstein" distance between the observed multi-dimensional node labels and their estimations, optimizing the label transport defined on graph edges. The estimations are parameterized by a GNN in which the optimal label transport may determine the graph edge weights optionally. By reformulating the strict constraint of the label transport to a Bregman divergence-based regularizer, we obtain the proposed Quasi-Wasserstein loss associated with two efficient solvers learning the GNN together with optimal label transport. When predicting node labels, our model combines the output of the GNN with the residual component provided by the optimal label transport, leading to a new transductive prediction paradigm. Experiments show that the proposed QW loss applies to various GNNs and helps to improve their performance in node-level classification and regression tasks.

Distributed quantum computing is a promising computational paradigm for performing computations that are beyond the reach of individual quantum devices. Privacy in distributed quantum computing is critical for maintaining confidentiality and protecting the data in the presence of untrusted computing nodes. In this work, we introduce novel blind quantum machine learning protocols based on the quantum bipartite correlator algorithm. Our protocols have reduced communication overhead while preserving the privacy of data from untrusted parties. We introduce robust algorithm-specific privacy-preserving mechanisms with low computational overhead that do not require complex cryptographic techniques. We then validate the effectiveness of the proposed protocols through complexity and privacy analysis. Our findings pave the way for advancements in distributed quantum computing, opening up new possibilities for privacy-aware machine learning applications in the era of quantum technologies.

Large Language Models (LLMs) are increasingly deployed as the backend for a variety of real-world applications called LLM-Integrated Applications. Multiple recent works showed that LLM-Integrated Applications are vulnerable to prompt injection attacks, in which an attacker injects malicious instruction/data into the input of those applications such that they produce results as the attacker desires. However, existing works are limited to case studies. As a result, the literature lacks a systematic understanding of prompt injection attacks and their defenses. We aim to bridge the gap in this work. In particular, we propose a general framework to formalize prompt injection attacks. Existing attacks, which are discussed in research papers and blog posts, are special cases in our framework. Our framework enables us to design a new attack by combining existing attacks. Moreover, we also propose a framework to systematize defenses against prompt injection attacks. Using our frameworks, we conduct a systematic evaluation on prompt injection attacks and their defenses with 10 LLMs and 7 tasks. We hope our frameworks can inspire future research in this field. Our code is available at https://github.com/liu00222/Open-Prompt-Injection.

Adaptable models could greatly benefit robotic agents operating in the real world, allowing them to deal with novel and varying conditions. While approaches such as Bayesian inference are well-studied frameworks for adapting models to evidence, we build on recent advances in deep generative models which have greatly affected many areas of robotics. Harnessing modern GPU acceleration, we investigate how to quickly adapt the sample generation of neural network models to observations in robotic tasks. We propose a simple and general method that is applicable to various deep generative models and robotic environments. The key idea is to quickly fine-tune the model by fitting it to generated samples matching the observed evidence, using the cross-entropy method. We show that our method can be applied to both autoregressive models and variational autoencoders, and demonstrate its usability in object shape inference from grasping, inverse kinematics calculation, and point cloud completion.

On-device machine learning (ML) enables the training process to exploit a massive amount of user-generated private data samples. To enjoy this benefit, inter-device communication overhead should be minimized. With this end, we propose federated distillation (FD), a distributed model training algorithm whose communication payload size is much smaller than a benchmark scheme, federated learning (FL), particularly when the model size is large. Moreover, user-generated data samples are likely to become non-IID across devices, which commonly degrades the performance compared to the case with an IID dataset. To cope with this, we propose federated augmentation (FAug), where each device collectively trains a generative model, and thereby augments its local data towards yielding an IID dataset. Empirical studies demonstrate that FD with FAug yields around 26x less communication overhead while achieving 95-98% test accuracy compared to FL.

In order to trust the predictions of a machine learning algorithm, it is necessary to understand the factors that contribute to those predictions. In the case of probabilistic and uncertainty-aware models, it is necessary to understand not only the reasons for the predictions themselves, but also the model's level of confidence in those predictions. In this paper, we show how existing methods in explainability can be extended to uncertainty-aware models and how such extensions can be used to understand the sources of uncertainty in a model's predictive distribution. In particular, by adapting permutation feature importance, partial dependence plots, and individual conditional expectation plots, we demonstrate that novel insights into model behaviour may be obtained and that these methods can be used to measure the impact of features on both the entropy of the predictive distribution and the log-likelihood of the ground truth labels under that distribution. With experiments using both synthetic and real-world data, we demonstrate the utility of these approaches in understanding both the sources of uncertainty and their impact on model performance.

In this article we present new results on neural networks with linear threshold activation functions. We precisely characterize the class of functions that are representable by such neural networks and show that 2 hidden layers are necessary and sufficient to represent any function representable in the class. This is a surprising result in the light of recent exact representability investigations for neural networks using other popular activation functions like rectified linear units (ReLU). We also give precise bounds on the sizes of the neural networks required to represent any function in the class. Finally, we design an algorithm to solve the empirical risk minimization (ERM) problem to global optimality for these neural networks with a fixed architecture. The algorithm's running time is polynomial in the size of the data sample, if the input dimension and the size of the network architecture are considered fixed constants. The algorithm is unique in the sense that it works for any architecture with any number of layers, whereas previous polynomial time globally optimal algorithms work only for very restricted classes of architectures. Using these insights, we propose a new class of neural networks that we call shortcut linear threshold networks. To the best of our knowledge, this way of designing neural networks has not been explored before in the literature. We show that these neural networks have several desirable theoretical properties.

Due to the increasing use of Machine Learning models in high stakes decision making settings, it has become increasingly important to have tools to understand how models arrive at decisions. Assuming a trained Supervised Classification model, explanations can be obtained via counterfactual analysis: a counterfactual explanation of an instance indicates how this instance should be minimally modified so that the perturbed instance is classified in the desired class by the Machine Learning classification model. Most of the Counterfactual Analysis literature focuses on the single-instance single-counterfactual setting, in which the analysis is done for one single instance to provide one single explanation. Taking a stakeholder's perspective, in this paper we introduce the so-called collective counterfactual explanations. By means of novel Mathematical Optimization models, we provide a counterfactual explanation for each instance in a group of interest, so that the total cost of the perturbations is minimized under some linking constraints. Making the process of constructing counterfactuals collective instead of individual enables us to detect the features that are critical to the entire dataset to have the individuals classified in the desired class. Our methodology allows for some instances to be treated individually, performing the collective counterfactual analysis for a fraction of records of the group of interest. This way, outliers are identified and handled appropriately. Under some assumptions on the classifier and the space in which counterfactuals are sought, finding collective counterfactuals is reduced to solving a convex quadratic linearly constrained mixed integer optimization problem, which, for datasets of moderate size, can be solved to optimality using existing solvers. The performance of our approach is illustrated on real-world datasets, demonstrating its usefulness.

Existing hierarchical forecasting techniques scale poorly when the number of time series increases. We propose to learn a coherent forecast for millions of time series with a single bottom-level forecast model by using a sparse loss function that directly optimizes the hierarchical product and/or temporal structure. The benefit of our sparse hierarchical loss function is that it provides practitioners a method of producing bottom-level forecasts that are coherent to any chosen cross-sectional or temporal hierarchy. In addition, removing the need for a post-processing step as required in traditional hierarchical forecasting techniques reduces the computational cost of the prediction phase in the forecasting pipeline. On the public M5 dataset, our sparse hierarchical loss function performs up to 10% (RMSE) better compared to the baseline loss function. We implement our sparse hierarchical loss function within an existing forecasting model at bol, a large European e-commerce platform, resulting in an improved forecasting performance of 2% at the product level. Finally, we found an increase in forecasting performance of about 5-10% when evaluating the forecasting performance across the cross-sectional hierarchies that we defined. These results demonstrate the usefulness of our sparse hierarchical loss applied to a production forecasting system at a major e-commerce platform.

While the accuracy-fairness trade-off has been frequently observed in the literature of fair machine learning, rigorous theoretical analyses have been scarce. To demystify this long-standing challenge, this work seeks to develop a theoretical framework by characterizing the shape of the accuracy-fairness trade-off Pareto frontier (FairFrontier), determined by a set of all optimal Pareto classifiers that no other classifiers can dominate. Specifically, we first demonstrate the existence of the trade-off in real-world scenarios and then propose four potential categories to characterize the important properties of the accuracy-fairness Pareto frontier. For each category, we identify the necessary conditions that lead to corresponding trade-offs. Experimental results on synthetic data suggest insightful findings of the proposed framework: (1) When sensitive attributes can be fully interpreted by non-sensitive attributes, FairFrontier is mostly continuous. (2) Accuracy can suffer a \textit{sharp} decline when over-pursuing fairness. (3) Eliminate the trade-off via a two-step streamlined approach. The proposed research enables an in-depth understanding of the accuracy-fairness trade-off, pushing current fair machine-learning research to a new frontier.

During Automated Program Repair (APR), it can be challenging to synthesize correct patches for real-world systems in general-purpose programming languages. Recent Large Language Models (LLMs) have been shown to be helpful "copilots" in assisting developers with various coding tasks, and have also been directly applied for patch synthesis. However, most LLMs treat programs as sequences of tokens, meaning that they are ignorant of the underlying semantics constraints of the target programming language. This results in plenty of statically invalid generated patches, impeding the practicality of the technique. Therefore, we propose Repilot, a framework to further copilot the AI "copilots" (i.e., LLMs) by synthesizing more valid patches during the repair process. Our key insight is that many LLMs produce outputs autoregressively (i.e., token by token), resembling human writing programs, which can be significantly boosted and guided through a Completion Engine. Repilot synergistically synthesizes a candidate patch through the interaction between an LLM and a Completion Engine, which 1) prunes away infeasible tokens suggested by the LLM and 2) proactively completes the token based on the suggestions provided by the Completion Engine. Our evaluation on a subset of the widely-used Defects4j 1.2 and 2.0 datasets shows that Repilot fixes 66 and 50 bugs, respectively, surpassing the best-performing baseline by 14 and 16 bugs fixed. More importantly, Repilot is capable of producing more valid and correct patches than the base LLM when given the same generation budget.

Implementations of Markov chain Monte Carlo (MCMC) methods need to confront two fundamental challenges: accurate representation of prior information and efficient evaluation of likelihoods. Principal component analysis (PCA) and related techniques can in some cases facilitate the definition and sampling of the prior distribution, as well as the training of accurate surrogate models, using for instance, polynomial chaos expansion (PCE). However, complex geological priors with sharp contrasts necessitate more complex dimensionality-reduction techniques, such as, deep generative models (DGMs). By sampling a low-dimensional prior probability distribution defined in the low-dimensional latent space of such a model, it becomes possible to efficiently sample the physical domain at the price of a generator that is typically highly non-linear. Training a surrogate that is capable of capturing intricate non-linear relationships between latent parameters and outputs of forward modeling presents a notable challenge. Indeed, while PCE models provide high accuracy when the input-output relationship can be effectively approximated by relatively low-degree multivariate polynomials, this condition is typically not met when employing latent variables derived from DGMs. In this contribution, we present a strategy combining the excellent reconstruction performances of a variational autoencoder (VAE) with the accuracy of PCA-PCE surrogate modeling in the context of Bayesian ground penetrating radar (GPR) traveltime tomography. Within the MCMC process, the parametrization of the VAE is leveraged for prior exploration and sample proposals. Concurrently, surrogate modeling is conducted using PCE, which operates on either globally or locally defined principal components of the VAE samples under examination.

Studying language models (LMs) in terms of well-understood formalisms allows us to precisely characterize their abilities and limitations. Previous work has investigated the representational capacity of recurrent neural network (RNN) LMs in terms of their capacity to recognize unweighted formal languages. However, LMs do not describe unweighted formal languages -- rather, they define probability distributions over strings. In this work, we study what classes of such probability distributions RNN LMs can represent, which allows us to make more direct statements about their capabilities. We show that simple RNNs are equivalent to a subclass of probabilistic finite-state automata, and can thus model a strict subset of probability distributions expressible by finite-state models. Furthermore, we study the space complexity of representing finite-state LMs with RNNs. We show that, to represent an arbitrary deterministic finite-state LM with $N$ states over an alphabet $\Sigma$, an RNN requires $\Omega\left(N |\Sigma|\right)$ neurons. These results present a first step towards characterizing the classes of distributions RNN LMs can represent and thus help us understand their capabilities and limitations.

We introduce Cosmos, a framework for object-centric world modeling that is designed for compositional generalization (CG), i.e., high performance on unseen input scenes obtained through the composition of known visual "atoms." The central insight behind Cosmos is the use of a novel form of neurosymbolic grounding. Specifically, the framework introduces two new tools: (i) neurosymbolic scene encodings, which represent each entity in a scene using a real vector computed using a neural encoder, as well as a vector of composable symbols describing attributes of the entity, and (ii) a neurosymbolic attention mechanism that binds these entities to learned rules of interaction. Cosmos is end-to-end differentiable; also, unlike traditional neurosymbolic methods that require representations to be manually mapped to symbols, it computes an entity's symbolic attributes using vision-language foundation models. Through an evaluation that considers two different forms of CG on an established blocks-pushing domain, we show that the framework establishes a new state-of-the-art for CG in world modeling.

Modern detection transformers (DETRs) use a set of object queries to predict a list of bounding boxes, sort them by their classification confidence scores, and select the top-ranked predictions as the final detection results for the given input image. A highly performant object detector requires accurate ranking for the bounding box predictions. For DETR-based detectors, the top-ranked bounding boxes suffer from less accurate localization quality due to the misalignment between classification scores and localization accuracy, thus impeding the construction of high-quality detectors. In this work, we introduce a simple and highly performant DETR-based object detector by proposing a series of rank-oriented designs, combinedly called Rank-DETR. Our key contributions include: (i) a rank-oriented architecture design that can prompt positive predictions and suppress the negative ones to ensure lower false positive rates, as well as (ii) a rank-oriented loss function and matching cost design that prioritizes predictions of more accurate localization accuracy during ranking to boost the AP under high IoU thresholds. We apply our method to improve the recent SOTA methods (e.g., H-DETR and DINO-DETR) and report strong COCO object detection results when using different backbones such as ResNet-$50$, Swin-T, and Swin-L, demonstrating the effectiveness of our approach. Code is available at \url{https://github.com/LeapLabTHU/Rank-DETR}.

Insurers usually turn to generalized linear models for modelling claim frequency and severity data. Due to their success in other fields, machine learning techniques are gaining popularity within the actuarial toolbox. Our paper contributes to the literature on frequency-severity insurance pricing with machine learning via deep learning structures. We present a benchmark study on four insurance data sets with frequency and severity targets in the presence of multiple types of input features. We compare in detail the performance of: a generalized linear model on binned input data, a gradient-boosted tree model, a feed-forward neural network (FFNN), and the combined actuarial neural network (CANN). Our CANNs combine a baseline prediction established with a GLM and GBM, respectively, with a neural network correction. We explain the data preprocessing steps with specific focus on the multiple types of input features typically present in tabular insurance data sets, such as postal codes, numeric and categorical covariates. Autoencoders are used to embed the categorical variables into the neural network and we explore their potential advantages in a frequency-severity setting. Finally, we construct global surrogate models for the neural nets' frequency and severity models. These surrogates enable the translation of the essential insights captured by the FFNNs or CANNs to GLMs. As such, a technical tariff table results that can easily be deployed in practice.

Modeling customer shopping intentions is a crucial task for e-commerce, as it directly impacts user experience and engagement. Thus, accurately understanding customer preferences is essential for providing personalized recommendations. Session-based recommendation, which utilizes customer session data to predict their next interaction, has become increasingly popular. However, existing session datasets have limitations in terms of item attributes, user diversity, and dataset scale. As a result, they cannot comprehensively capture the spectrum of user behaviors and preferences. To bridge this gap, we present the Amazon Multilingual Multi-locale Shopping Session Dataset, namely Amazon-M2. It is the first multilingual dataset consisting of millions of user sessions from six different locales, where the major languages of products are English, German, Japanese, French, Italian, and Spanish. Remarkably, the dataset can help us enhance personalization and understanding of user preferences, which can benefit various existing tasks as well as enable new tasks. To test the potential of the dataset, we introduce three tasks in this work: (1) next-product recommendation, (2) next-product recommendation with domain shifts, and (3) next-product title generation. With the above tasks, we benchmark a range of algorithms on our proposed dataset, drawing new insights for further research and practice. In addition, based on the proposed dataset and tasks, we hosted a competition in the KDD CUP 2023 and have attracted thousands of users and submissions. The winning solutions and the associated workshop can be accessed at our website https://kddcup23.github.io/.

Federated Learning (FL) is a machine learning paradigm, which enables multiple and decentralized clients to collaboratively train a model under the orchestration of a central aggregator. Traditional FL solutions rely on the trust assumption of the centralized aggregator, which forms cohorts of clients in a fair and honest manner. However, a malicious aggregator, in reality, could abandon and replace the client's training models, or launch Sybil attacks to insert fake clients. Such malicious behaviors give the aggregator more power to control clients in the FL setting and determine the final training results. In this work, we introduce zkFL, which leverages zero-knowledge proofs (ZKPs) to tackle the issue of a malicious aggregator during the training model aggregation process. To guarantee the correct aggregation results, the aggregator needs to provide a proof per round. The proof can demonstrate to the clients that the aggregator executes the intended behavior faithfully. To further reduce the verification cost of clients, we employ a blockchain to handle the proof in a zero-knowledge way, where miners (i.e., the nodes validating and maintaining the blockchain data) can verify the proof without knowing the clients' local and aggregated models. The theoretical analysis and empirical results show that zkFL can achieve better security and privacy than traditional FL, without modifying the underlying FL network structure or heavily compromising the training speed.

Unraveling the emergence of collective learning in systems of coupled artificial neural networks is an endeavor with broader implications for physics, machine learning, neuroscience and society. Here we introduce a minimal model that condenses several recent decentralized algorithms by considering a competition between two terms: the local learning dynamics in the parameters of each neural network unit, and a diffusive coupling among units that tends to homogenize the parameters of the ensemble. We derive the coarse-grained behavior of our model via an effective theory for linear networks that we show is analogous to a deformed Ginzburg-Landau model with quenched disorder. This framework predicts (depth-dependent) disorder-order-disorder phase transitions in the parameters' solutions that reveal the onset of a collective learning phase, along with a depth-induced delay of the critical point and a robust shape of the microscopic learning path. We validate our theory in realistic ensembles of coupled nonlinear networks trained in the MNIST dataset under privacy constraints. Interestingly, experiments confirm that individual networks -- trained only with private data -- can fully generalize to unseen data classes when the collective learning phase emerges. Our work elucidates the physics of collective learning and contributes to the mechanistic interpretability of deep learning in decentralized settings.

This paper analyses a set of simple adaptations that transform standard message-passing Graph Neural Networks (GNN) into provably powerful directed multigraph neural networks. The adaptations include multigraph port numbering, ego IDs, and reverse message passing. We prove that the combination of these theoretically enables the detection of any directed subgraph pattern. To validate the effectiveness of our proposed adaptations in practice, we conduct experiments on synthetic subgraph detection tasks, which demonstrate outstanding performance with almost perfect results. Moreover, we apply our proposed adaptations to two financial crime analysis tasks. We observe dramatic improvements in detecting money laundering transactions, improving the minority-class F1 score of a standard message-passing GNN by up to 30%, and closely matching or outperforming tree-based and GNN baselines. Similarly impressive results are observed on a real-world phishing detection dataset, boosting three standard GNNs' F1 scores by around 15% and outperforming all baselines.

Stochastic processes defined on integer valued state spaces are popular within the physical and biological sciences. These models are necessary for capturing the dynamics of small systems where the individual nature of the populations cannot be ignored and stochastic effects are important. The inference of the parameters of such models, from time series data, is difficult due to intractability of the likelihood; current methods, based on simulations of the underlying model, can be so computationally expensive as to be prohibitive. In this paper we construct a neural likelihood approximation for integer valued time series data using causal convolutions, which allows us to evaluate the likelihood of the whole time series in parallel. We demonstrate our method by performing inference on a number of ecological and epidemiological models, showing that we can accurately approximate the true posterior while achieving significant computational speed ups in situations where current methods struggle.

We propose inverse renormalization group transformations to construct approximate configurations for lattice volumes that have not yet been accessed by supercomputers or large-scale simulations in the study of spin glasses. Specifically, starting from lattices of volume $V=8^{3}$ in the case of the three-dimensional Edwards-Anderson model we employ machine learning algorithms to construct rescaled lattices up to $V'=128^{3}$, which we utilize to extract two critical exponents. We conclude by discussing how to incorporate numerical exactness within inverse renormalization group approaches of disordered systems, thus opening up the opportunity to explore a sustainable and energy-efficient generation of exact configurations for increasing lattice volumes without the use of dedicated supercomputers.

Compressing neural networks is a key step when deploying models for real-time or embedded applications. Factorizing the model's matrices using low-rank approximations is a promising method for achieving compression. While it is possible to set the rank before training, this approach is neither flexible nor optimal. In this work, we propose a post-training rank-selection method called Rank-Tuning that selects a different rank for each matrix. Used in combination with training adaptations, our method achieves high compression rates with no or little performance degradation. Our numerical experiments on signal processing tasks show that we can compress recurrent neural networks up to 14x with at most 1.4% relative performance reduction.

Traffic state forecasting is crucial for traffic management and control strategies, as well as user- and system-level decision making in the transportation network. While traffic forecasting has been approached with a variety of techniques over the last couple of decades, most approaches simply rely on endogenous traffic variables for state prediction, despite the evidence that exogenous factors can significantly impact traffic conditions. This paper proposes a multi-dimensional spatio-temporal graph attention-based traffic prediction approach (M-STGAT), which predicts traffic based on past observations of speed, along with lane closure events, temperature, and visibility across the transportation network. The approach is based on a graph attention network architecture, which also learns based on the structure of the transportation network on which these variables are observed. Numerical experiments are performed using traffic speed and lane closure data from the California Department of Transportation (Caltrans) Performance Measurement System (PeMS). The corresponding weather data were downloaded from the National Oceanic and Atmospheric Administration (NOOA) Automated Surface Observing Systems (ASOS). For comparison, the numerical experiments implement three alternative models which do not allow for the multi-dimensional input. The M-STGAT is shown to outperform the three alternative models, when performing tests using our primary data set for prediction with a 30-, 45-, and 60-minute prediction horizon, in terms of three error measures: Mean Absolute Error (MAE), Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE). However, the model's transferability can vary for different transfer data sets and this aspect may require further investigation.

Time is one of the crucial factors in real-world question answering (QA) problems. However, language models have difficulty understanding the relationships between time specifiers, such as 'after' and 'before', and numbers, since existing QA datasets do not include sufficient time expressions. To address this issue, we propose a Time-Context aware Question Answering (TCQA) framework. We suggest a Time-Context dependent Span Extraction (TCSE) task, and build a time-context dependent data generation framework for model training. Moreover, we present a metric to evaluate the time awareness of the QA model using TCSE. The TCSE task consists of a question and four sentence candidates classified as correct or incorrect based on time and context. The model is trained to extract the answer span from the sentence that is both correct in time and context. The model trained with TCQA outperforms baseline models up to 8.5 of the F1-score in the TimeQA dataset. Our dataset and code are available at https://github.com/sonjbin/TCQA

We introduce Voyager, the first LLM-powered embodied lifelong learning agent in Minecraft that continuously explores the world, acquires diverse skills, and makes novel discoveries without human intervention. Voyager consists of three key components: 1) an automatic curriculum that maximizes exploration, 2) an ever-growing skill library of executable code for storing and retrieving complex behaviors, and 3) a new iterative prompting mechanism that incorporates environment feedback, execution errors, and self-verification for program improvement. Voyager interacts with GPT-4 via blackbox queries, which bypasses the need for model parameter fine-tuning. The skills developed by Voyager are temporally extended, interpretable, and compositional, which compounds the agent's abilities rapidly and alleviates catastrophic forgetting. Empirically, Voyager shows strong in-context lifelong learning capability and exhibits exceptional proficiency in playing Minecraft. It obtains 3.3x more unique items, travels 2.3x longer distances, and unlocks key tech tree milestones up to 15.3x faster than prior SOTA. Voyager is able to utilize the learned skill library in a new Minecraft world to solve novel tasks from scratch, while other techniques struggle to generalize. We open-source our full codebase and prompts at https://voyager.minedojo.org/.

The Orthogonal-Time-Frequency-Space (OTFS) signaling is known to be resilient to doubly-dispersive channels, which impacts high mobility scenarios. On the other hand, the Orthogonal-Frequency-Division-Multiplexing (OFDM) waveforms enjoy the benefits of the reuse of legacy architectures, simplicity of receiver design, and low-complexity detection. Several studies that compare the performance of OFDM and OTFS have indicated mixed outcomes due to the plethora of system parameters at play beyond high-mobility conditions. In this work, we exemplify this observation using simulations and propose a deep neural network (DNN)-based adaptation scheme to switch between using either an OTFS or OFDM signal processing chain at the transmitter and receiver for optimal mean-squared-error (MSE) performance. The DNN classifier is trained to switch between the two schemes by observing the channel condition, received SNR, and modulation format. We compare the performance of the OTFS, OFDM, and the proposed switched-waveform scheme. The simulations indicate superior performance with the proposed scheme with a well-trained DNN, thus improving the MSE performance of the communication significantly.

The cause-to-effect analysis can help us decompose all the likely causes of a problem, such as an undesirable business situation or unintended harm to the individual(s). This implies that we can identify how the problems are inherited, rank the causes to help prioritize fixes, simplify a complex problem and visualize them. In the context of machine learning (ML), one can use cause-to-effect analysis to understand the reason for the biased behavior of the system. For example, we can examine the root causes of biases by checking each feature for a potential cause of bias in the model. To approach this, one can apply small changes to a given feature or a pair of features in the data, following some guidelines and observing how it impacts the decision made by the model (i.e., model prediction). Therefore, we can use cause-to-effect analysis to identify the potential bias-inducing features, even when these features are originally are unknown. This is important since most current methods require a pre-identification of sensitive features for bias assessment and can actually miss other relevant bias-inducing features, which is why systematic identification of such features is necessary. Moreover, it often occurs that to achieve an equitable outcome, one has to take into account sensitive features in the model decision. Therefore, it should be up to the domain experts to decide based on their knowledge of the context of a decision whether bias induced by specific features is acceptable or not. In this study, we propose an approach for systematically identifying all bias-inducing features of a model to help support the decision-making of domain experts. We evaluated our technique using four well-known datasets to showcase how our contribution can help spearhead the standard procedure when developing, testing, maintaining, and deploying fair/equitable machine learning systems.

Classes of target functions containing a large number of approximately orthogonal elements are known to be hard to learn by the Statistical Query algorithms. Recently this classical fact re-emerged in a theory of gradient-based optimization of neural networks. In the novel framework, the hardness of a class is usually quantified by the variance of the gradient with respect to a random choice of a target function. A set of functions of the form $x\to ax \bmod p$, where $a$ is taken from ${\mathbb Z}_p$, has attracted some attention from deep learning theorists and cryptographers recently. This class can be understood as a subset of $p$-periodic functions on ${\mathbb Z}$ and is tightly connected with a class of high-frequency periodic functions on the real line. We present a mathematical analysis of limitations and challenges associated with using gradient-based learning techniques to train a high-frequency periodic function or modular multiplication from examples. We highlight that the variance of the gradient is negligibly small in both cases when either a frequency or the prime base $p$ is large. This in turn prevents such a learning algorithm from being successful.

The prevalence and strong capability of large language models (LLMs) present significant safety and ethical risks if exploited by malicious users. To prevent the potentially deceptive usage of LLMs, recent works have proposed algorithms to detect LLM-generated text and protect LLMs. In this paper, we investigate the robustness and reliability of these LLM detectors under adversarial attacks. We study two types of attack strategies: 1) replacing certain words in an LLM's output with their synonyms given the context; 2) automatically searching for an instructional prompt to alter the writing style of the generation. In both strategies, we leverage an auxiliary LLM to generate the word replacements or the instructional prompt. Different from previous works, we consider a challenging setting where the auxiliary LLM can also be protected by a detector. Experiments reveal that our attacks effectively compromise the performance of all detectors in the study with plausible generations, underscoring the urgent need to improve the robustness of LLM-generated text detection systems.

Advances in the field of vision-language contrastive learning have made it possible for many downstream applications to be carried out efficiently and accurately by simply taking the dot product between image and text representations. One of the most representative approaches proposed recently known as CLIP has garnered widespread adoption due to its effectiveness. CLIP is trained with an InfoNCE loss that takes into account both positive and negative samples to help learn a much more robust representation space. This paper reveals that the common downstream practice of taking a dot product is only a zeroth-order approximation of the optimization goal, resulting in a loss of information during test-time. Intuitively, since the model has been optimized based on the InfoNCE loss, test-time procedures should also be in alignment. The question lies in how one can retrieve any semblance of negative samples information during inference in a computationally efficient way. To this end, we propose Distribution Normalization (DN), where we approximate the mean representation of a batch of test samples and use such a mean to represent what would be analogous to negative samples in the InfoNCE loss. DN requires no retraining or fine-tuning and can be effortlessly applied during inference. Extensive experiments on a wide variety of downstream tasks exhibit a clear advantage of DN over the dot product on top of other existing test-time augmentation methods.

Existing private synthetic data generation algorithms are agnostic to downstream tasks. However, end users may have specific requirements that the synthetic data must satisfy. Failure to meet these requirements could significantly reduce the utility of the data for downstream use. We introduce a post-processing technique that improves the utility of the synthetic data with respect to measures selected by the end user, while preserving strong privacy guarantees and dataset quality. Our technique involves resampling from the synthetic data to filter out samples that do not meet the selected utility measures, using an efficient stochastic first-order algorithm to find optimal resampling weights. Through comprehensive numerical experiments, we demonstrate that our approach consistently improves the utility of synthetic data across multiple benchmark datasets and state-of-the-art synthetic data generation algorithms.

Physics-informed neural networks have emerged as a coherent framework for building predictive models that combine statistical patterns with domain knowledge. The underlying notion is to enrich the optimization loss function with known relationships to constrain the space of possible solutions. Hydrodynamic simulations are a core constituent of modern cosmology, while the required computations are both expensive and time-consuming. At the same time, the comparatively fast simulation of dark matter requires fewer resources, which has led to the emergence of machine learning algorithms for baryon inpainting as an active area of research; here, recreating the scatter found in hydrodynamic simulations is an ongoing challenge. This paper presents the first application of physics-informed neural networks to baryon inpainting by combining advances in neural network architectures with physical constraints, injecting theory on baryon conversion efficiency into the model loss function. We also introduce a punitive prediction comparison based on the Kullback-Leibler divergence, which enforces scatter reproduction. By simultaneously extracting the complete set of baryonic properties for the Simba suite of cosmological simulations, our results demonstrate improved accuracy of baryonic predictions based on dark matter halo properties, successful recovery of the fundamental metallicity relation, and retrieve scatter that traces the target simulation's distribution.

The digitization of manufacturing processes enables promising applications for machine learning-assisted quality assurance. A widely used manufacturing process that can strongly benefit from data-driven solutions is \ac{GMAW}. The welding process is characterized by complex cause-effect relationships between material properties, process conditions and weld quality. In non-laboratory environments with frequently changing process parameters, accurate determination of weld quality by destructive testing is economically unfeasible. Deep learning offers the potential to identify the relationships in available process data and predict the weld quality from process observations. In this paper, we present a concept for a deep learning based predictive quality system in \ac{GMAW}. At its core, the concept involves a pipeline consisting of four major phases: collection and management of multi-sensor data (e.g. current and voltage), real-time processing and feature engineering of the time series data by means of autoencoders, training and deployment of suitable recurrent deep learning models for quality predictions, and model evolutions under changing process conditions using continual learning. The concept provides the foundation for future research activities in which we will realize an online predictive quality system for running production.

Artificial intelligence (AI) systems possess significant potential to drive societal progress. However, their deployment often faces obstacles due to substantial safety concerns. Safe reinforcement learning (SafeRL) emerges as a solution to optimize policies while simultaneously adhering to multiple constraints, thereby addressing the challenge of integrating reinforcement learning in safety-critical scenarios. In this paper, we present an environment suite called Safety-Gymnasium, which encompasses safety-critical tasks in both single and multi-agent scenarios, accepting vector and vision-only input. Additionally, we offer a library of algorithms named Safe Policy Optimization (SafePO), comprising 16 state-of-the-art SafeRL algorithms. This comprehensive library can serve as a validation tool for the research community. By introducing this benchmark, we aim to facilitate the evaluation and comparison of safety performance, thus fostering the development of reinforcement learning for safer, more reliable, and responsible real-world applications. The website of this project can be accessed at https://sites.google.com/view/safety-gymnasium.

Large decoder-only language models (LMs) can be largely improved in terms of perplexity by retrieval (e.g., RETRO), but its impact on text generation quality and downstream task accuracy is unclear. Thus, it is still an open question: shall we pretrain large autoregressive LMs with retrieval? To answer it, we perform a comprehensive study on a scalable pre-trained retrieval-augmented LM (i.e., RETRO) compared with standard GPT and retrieval-augmented GPT incorporated at fine-tuning or inference stages. We first provide the recipe to reproduce RETRO up to 9.5B parameters while retrieving a text corpus with 330B tokens. Based on that, we have the following novel findings: i) RETRO outperforms GPT on text generation with much less degeneration (i.e., repetition), moderately higher factual accuracy, and slightly lower toxicity with a nontoxic retrieval database. ii) On the LM Evaluation Harness benchmark, RETRO largely outperforms GPT on knowledge-intensive tasks, but is on par with GPT on other tasks. Furthermore, we introduce a simple variant of the model, RETRO++, which largely improves open-domain QA results of original RETRO (e.g., EM score +8.6 on Natural Question) and significantly outperforms retrieval-augmented GPT in both fine-tuning and zero-shot evaluation settings. Our findings highlight the promising direction of pretraining autoregressive LMs with retrieval as future foundation models. We release our implementation at: https://github.com/NVIDIA/Megatron-LM#retro.

Deep Learning models, such as those used in an autonomous vehicle are vulnerable to adversarial attacks where an attacker could place an adversarial object in the environment, leading to mis-classification. Generating these adversarial objects in the digital space has been extensively studied, however successfully transferring these attacks from the digital realm to the physical realm has proven challenging when controlling for real-world environmental factors. In response to these limitations, we introduce REVAMP, an easy-to-use Python library that is the first-of-its-kind tool for creating attack scenarios with arbitrary objects and simulating realistic environmental factors, lighting, reflection, and refraction. REVAMP enables researchers and practitioners to swiftly explore various scenarios within the digital realm by offering a wide range of configurable options for designing experiments and using differentiable rendering to reproduce physically plausible adversarial objects. We will demonstrate and invite the audience to try REVAMP to produce an adversarial texture on a chosen object while having control over various scene parameters. The audience will choose a scene, an object to attack, the desired attack class, and the number of camera positions to use. Then, in real time, we show how this altered texture causes the chosen object to be mis-classified, showcasing the potential of REVAMP in real-world scenarios. REVAMP is open-source and available at https://github.com/poloclub/revamp.

Diffusion models are powerful, but they require a lot of time and data to train. We propose Patch Diffusion, a generic patch-wise training framework, to significantly reduce the training time costs while improving data efficiency, which thus helps democratize diffusion model training to broader users. At the core of our innovations is a new conditional score function at the patch level, where the patch location in the original image is included as additional coordinate channels, while the patch size is randomized and diversified throughout training to encode the cross-region dependency at multiple scales. Sampling with our method is as easy as in the original diffusion model. Through Patch Diffusion, we could achieve $\mathbf{\ge 2\times}$ faster training, while maintaining comparable or better generation quality. Patch Diffusion meanwhile improves the performance of diffusion models trained on relatively small datasets, $e.g.$, as few as 5,000 images to train from scratch. We achieve outstanding FID scores in line with state-of-the-art benchmarks: 1.77 on CelebA-64$\times$64, 1.93 on AFHQv2-Wild-64$\times$64, and 2.72 on ImageNet-256$\times$256. We share our code and pre-trained models at https://github.com/Zhendong-Wang/Patch-Diffusion.

The training and generalization dynamics of the Transformer's core mechanism, namely the Attention mechanism, remain under-explored. Besides, existing analyses primarily focus on single-head attention. Inspired by the demonstrated benefits of overparameterization when training fully-connected networks, we investigate the potential optimization and generalization advantages of using multiple attention heads. Towards this goal, we derive convergence and generalization guarantees for gradient-descent training of a single-layer multi-head self-attention model, under a suitable realizability condition on the data. We then establish primitive conditions on the initialization that ensure realizability holds. Finally, we demonstrate that these conditions are satisfied for a simple tokenized-mixture model. We expect the analysis can be extended to various data-model and architecture variations.

Linear temporal logic (LTL) and omega-regular objectives -- a superset of LTL -- have seen recent use as a way to express non-Markovian objectives in reinforcement learning. We introduce a model-based probably approximately correct (PAC) learning algorithm for omega-regular objectives in Markov decision processes. Unlike prior approaches, our algorithm learns from sampled trajectories of the system and does not require prior knowledge of the system's topology.

We explain the methodology used to create the data submitted to HuMob Challenge, a data analysis competition for human mobility prediction. We adopted a personalized model to predict the individual's movement trajectory from their data, instead of predicting from the overall movement, based on the hypothesis that human movement is unique to each person. We devised the features such as the date and time, activity time, days of the week, time of day, and frequency of visits to POI (Point of Interest). As additional features, we incorporated the movement of other individuals with similar behavior patterns through the employment of clustering. The machine learning model we adopted was the Support Vector Regression (SVR). We performed accuracy through offline assessment and carried out feature selection and parameter tuning. Although overall dataset provided consists of 100,000 users trajectory, our method use only 20,000 target users data, and do not need to use other 80,000 data. Despite the personalized model's traditional feature engineering approach, this model yields reasonably good accuracy with lower computational cost.

Diffusion models have risen as a powerful tool in robotics due to their flexibility and multi-modality. While some of these methods effectively address complex problems, they often depend heavily on inference-time obstacle detection and require additional equipment. Addressing these challenges, we present a method that, during inference time, simultaneously generates only reachable goals and plans motions that avoid obstacles, all from a single visual input. Central to our approach is the novel use of a collision-avoiding diffusion kernel for training. Through evaluations against behavior-cloning and classical diffusion models, our framework has proven its robustness. It is particularly effective in multi-modal environments, navigating toward goals and avoiding unreachable ones blocked by obstacles, while ensuring collision avoidance.

We propose DyGFormer, a new Transformer-based architecture for dynamic graph learning. DyGFormer is conceptually simple and only needs to learn from nodes' historical first-hop interactions by: (1) a neighbor co-occurrence encoding scheme that explores the correlations of the source node and destination node based on their historical sequences; (2) a patching technique that divides each sequence into multiple patches and feeds them to Transformer, allowing the model to effectively and efficiently benefit from longer histories. We also introduce DyGLib, a unified library with standard training pipelines, extensible coding interfaces, and comprehensive evaluating protocols to promote reproducible, scalable, and credible dynamic graph learning research. By performing exhaustive experiments on thirteen datasets for dynamic link prediction and dynamic node classification tasks, we find that DyGFormer achieves state-of-the-art performance on most of the datasets, demonstrating its effectiveness in capturing nodes' correlations and long-term temporal dependencies. Moreover, some results of baselines are inconsistent with previous reports, which may be caused by their diverse but less rigorous implementations, showing the importance of DyGLib. All the used resources are publicly available at https://github.com/yule-BUAA/DyGLib.

Despite the recent growth of theoretical studies and empirical successes of neural networks, gradient backpropagation is still the most widely used algorithm for training such networks. On the one hand, we have deterministic or full gradient (FG) approaches that have a cost proportional to the amount of training data used but have a linear convergence rate, and on the other hand, stochastic gradient (SG) methods that have a cost independent of the size of the dataset, but have a less optimal convergence rate than the determinist approaches. To combine the cost of the stochastic approach with the convergence rate of the deterministic approach, a stochastic average gradient (SAG) has been proposed. SAG is a method for optimizing the sum of a finite number of smooth convex functions. Like SG methods, the SAG method's iteration cost is independent of the number of terms in the sum. In this work, we propose to compare SAG to some standard optimizers used in machine learning. SAG converges faster than other optimizers on simple toy problems and performs better than many other optimizers on simple machine learning problems. We also propose a combination of SAG with the momentum algorithm and Adam. These combinations allow empirically higher speed and obtain better performance than the other methods, especially when the landscape of the function to optimize presents obstacles or is ill-conditioned.

Entropy maximization and free energy minimization are general physical principles for modeling the dynamics of various physical systems. Notable examples include modeling decision-making within the brain using the free-energy principle, optimizing the accuracy-complexity trade-off when accessing hidden variables with the information bottleneck principle (Tishby et al., 2000), and navigation in random environments using information maximization (Vergassola et al., 2007). Built on this principle, we propose a new class of bandit algorithms that maximize an approximation to the information of a key variable within the system. To this end, we develop an approximated analytical physics-based representation of an entropy to forecast the information gain of each action and greedily choose the one with the largest information gain. This method yields strong performances in classical bandit settings. Motivated by its empirical success, we prove its asymptotic optimality for the two-armed bandit problem with Gaussian rewards. Owing to its ability to encompass the system's properties in a global physical functional, this approach can be efficiently adapted to more complex bandit settings, calling for further investigation of information maximization approaches for multi-armed bandit problems.

Binary classification is a fundamental task in machine learning, with applications spanning various scientific domains. Whether scientists are conducting fundamental research or refining practical applications, they typically assess and rank classification techniques based on performance metrics such as accuracy, sensitivity, and specificity. However, reported performance scores may not always serve as a reliable basis for research ranking. This can be attributed to undisclosed or unconventional practices related to cross-validation, typographical errors, and other factors. In a given experimental setup, with a specific number of positive and negative test items, most performance scores can assume specific, interrelated values. In this paper, we introduce numerical techniques to assess the consistency of reported performance scores and the assumed experimental setup. Importantly, the proposed approach does not rely on statistical inference but uses numerical methods to identify inconsistencies with certainty. Through three different applications related to medicine, we demonstrate how the proposed techniques can effectively detect inconsistencies, thereby safeguarding the integrity of research fields. To benefit the scientific community, we have made the consistency tests available in an open-source Python package.

Sequential transfer optimization (STO), which aims to improve the optimization performance on a task of interest by exploiting the knowledge captured from several previously-solved optimization tasks stored in a database, has been gaining increasing research attention over the years. However, despite the remarkable advances in algorithm design, the development of a systematic benchmark suite for comprehensive comparisons of STO algorithms received far less attention. Existing test problems are either simply generated by assembling other benchmark functions or extended from specific practical problems with limited scalability. The relationships between the optimal solutions of the source and target tasks in these problems are also often manually configured, limiting their ability to model different similarity relationships presented in real-world problems. Consequently, the good performance achieved by an algorithm on these problems might be biased and hard to be generalized to other problems. In light of the above, in this study, we first introduce four concepts for characterizing STO problems and present an important problem feature, namely similarity distribution, which quantitatively delineates the relationship between the optima of the source and target tasks. Then, we present the general design guidelines of STO problems and a particular STO problem generator with good scalability. Specifically, the similarity distribution of a problem can be easily customized, enabling a continuous spectrum of representation of the diverse similarity relationships of real-world problems. Lastly, a benchmark suite with 12 STO problems featured by a variety of customized similarity relationships is developed using the proposed generator. The source code of the problem generator is available at https://github.com/XmingHsueh/STOP-G.

This letter proposes a novel anti-interference technique, semantic interference cancellation (SemantIC), for enhancing information quality towards the sixth-generation (6G) wireless networks. SemantIC only requires the receiver to concatenate the channel decoder with a semantic auto-encoder. This constructs a turbo loop which iteratively and alternately eliminates noise in the signal domain and the semantic domain. From the viewpoint of network information theory, the neural network of the semantic auto-encoder stores side information by training, and provides side information in iterative decoding, as an implementation of the Wyner-Ziv theorem. Simulation results verify the performance improvement by SemantIC without extra channel resource cost.

Although remarkable progress has been achieved in preventing large language model (LLM) hallucinations using instruction tuning and retrieval augmentation, it remains challenging to measure the reliability of LLMs using human-crafted evaluation data which is not available for many tasks and domains and could suffer from data leakage. Inspired by adversarial machine learning, this paper aims to develop a method of automatically generating evaluation data by appropriately modifying existing data on which LLMs behave faithfully. Specifically, this paper presents AutoDebug, an LLM-based framework to use prompting chaining to generate transferable adversarial attacks in the form of question-answering examples. We seek to understand the extent to which these examples trigger the hallucination behaviors of LLMs. We implement AutoDebug using ChatGPT and evaluate the resulting two variants of a popular open-domain question-answering dataset, Natural Questions (NQ), on a collection of open-source and proprietary LLMs under various prompting settings. Our generated evaluation data is human-readable and, as we show, humans can answer these modified questions well. Nevertheless, we observe pronounced accuracy drops across multiple LLMs including GPT-4. Our experimental results show that LLMs are likely to hallucinate in two categories of question-answering scenarios where (1) there are conflicts between knowledge given in the prompt and their parametric knowledge, or (2) the knowledge expressed in the prompt is complex. Finally, we find that the adversarial examples generated by our method are transferable across all considered LLMs. The examples generated by a small model can be used to debug a much larger model, making our approach cost-effective.

We study the problem of lifelong graph learning in an open-world scenario, where a model needs to deal with new tasks and potentially unknown classes. We utilize Out-of-Distribution (OOD) detection methods to recognize new classes and adapt existing non-graph OOD detection methods to graph data. Crucially, we suggest performing new class detection by combining OOD detection methods with information aggregated from the graph neighborhood. Most OOD detection methods avoid determining a crisp threshold for deciding whether a vertex is OOD. To tackle this problem, we propose a Weakly-supervised Relevance Feedback (Open-WRF) method, which decreases the sensitivity to thresholds in OOD detection. We evaluate our approach on six benchmark datasets. Our results show that the proposed neighborhood aggregation method for OOD scores outperforms existing methods independent of the underlying graph neural network. Furthermore, we demonstrate that our Open-WRF method is more robust to threshold selection and analyze the influence of graph neighborhood on OOD detection. The aggregation and threshold methods are compatible with arbitrary graph neural networks and OOD detection methods, making our approach versatile and applicable to many real-world applications.

Argumentation is a very active research field of Artificial Intelligence concerned with the representation and evaluation of arguments used in dialogues between humans and/or artificial agents. Acceptability semantics of formal argumentation systems define the criteria for the acceptance or rejection of arguments. Several software systems, known as argumentation solvers, have been developed to compute the accepted/rejected arguments using such criteria. These include systems that learn to identify the accepted arguments using non-interpretable methods. In this paper we present a novel framework, which uses an Inductive Logic Programming approach to learn the acceptability semantics for several abstract and structured argumentation frameworks in an interpretable way. Through an empirical evaluation we show that our framework outperforms existing argumentation solvers, thus opening up new future research directions in the area of formal argumentation and human-machine dialogues.

This paper examines the use of operator-theoretic approaches to the analysis of chaotic systems through the lens of their unstable periodic orbits (UPOs). Our approach involves three data-driven steps for detecting, identifying, and stabilizing UPOs. We demonstrate the use of kernel integral operators within delay coordinates as an innovative method for UPO detection. For identifying the dynamic behavior associated with each individual UPO, we utilize the Koopman operator to present the dynamics as linear equations in the space of Koopman eigenfunctions. This allows for characterizing the chaotic attractor by investigating its principal dynamical modes across varying UPOs. We extend this methodology into an interpretable machine learning framework aimed at stabilizing strange attractors on their UPOs. To illustrate the efficacy of our approach, we apply it to the Lorenz attractor as a case study.

We present a novel meta-learning approach for 6D pose estimation on unknown objects. In contrast to ``instance-level" and ``category-level" pose estimation methods, our algorithm learns object representation in a category-agnostic way, which endows it with strong generalization capabilities across object categories. Specifically, we employ a neural process-based meta-learning approach to train an encoder to capture texture and geometry of an object in a latent representation, based on very few RGB-D images and ground-truth keypoints. The latent representation is then used by a simultaneously meta-trained decoder to predict the 6D pose of the object in new images. Furthermore, we propose a novel geometry-aware decoder for the keypoint prediction using a Graph Neural Network (GNN), which explicitly takes geometric constraints specific to each object into consideration. To evaluate our algorithm, extensive experiments are conducted on the \linemod dataset, and on our new fully-annotated synthetic datasets generated from Multiple Categories in Multiple Scenes (MCMS). Experimental results demonstrate that our model performs well on unseen objects with very different shapes and appearances. Remarkably, our model also shows robust performance on occluded scenes although trained fully on data without occlusion. To our knowledge, this is the first work exploring \textbf{cross-category level} 6D pose estimation.

The fast-paced development of machine learning (ML) methods coupled with its increasing adoption in research poses challenges for researchers without extensive training in ML. In neuroscience, for example, ML can help understand brain-behavior relationships, diagnose diseases, and develop biomarkers using various data sources like magnetic resonance imaging and electroencephalography. The primary objective of ML is to build models that can make accurate predictions on unseen data. Researchers aim to prove the existence of such generalizable models by evaluating performance using techniques such as cross-validation (CV), which uses systematic subsampling to estimate the generalization performance. Choosing a CV scheme and evaluating an ML pipeline can be challenging and, if used improperly, can lead to overestimated results and incorrect interpretations. We created julearn, an open-source Python library, that allow researchers to design and evaluate complex ML pipelines without encountering in common pitfalls. In this manuscript, we present the rationale behind julearn's design, its core features, and showcase three examples of previously-published research projects that can be easily implemented using this novel library. Julearn aims to simplify the entry into the ML world by providing an easy-to-use environment with built in guards against some of the most common ML pitfalls. With its design, unique features and simple interface, it poses as a useful Python-based library for research projects.

We propose a differentiable vertex fitting algorithm that can be used for secondary vertex fitting, and that can be seamlessly integrated into neural networks for jet flavour tagging. Vertex fitting is formulated as an optimization problem where gradients of the optimized solution vertex are defined through implicit differentiation and can be passed to upstream or downstream neural network components for network training. More broadly, this is an application of differentiable programming to integrate physics knowledge into neural network models in high energy physics. We demonstrate how differentiable secondary vertex fitting can be integrated into larger transformer-based models for flavour tagging and improve heavy flavour jet classification.

Existing analyses of neural network training often operate under the unrealistic assumption of an extremely small learning rate. This lies in stark contrast to practical wisdom and empirical studies, such as the work of J. Cohen et al. (ICLR 2021), which exhibit startling new phenomena (the "edge of stability" or "unstable convergence") and potential benefits for generalization in the large learning rate regime. Despite a flurry of recent works on this topic, however, the latter effect is still poorly understood. In this paper, we take a step towards understanding genuinely non-convex training dynamics with large learning rates by performing a detailed analysis of gradient descent for simplified models of two-layer neural networks. For these models, we provably establish the edge of stability phenomenon and discover a sharp phase transition for the step size below which the neural network fails to learn "threshold-like" neurons (i.e., neurons with a non-zero first-layer bias). This elucidates one possible mechanism by which the edge of stability can in fact lead to better generalization, as threshold neurons are basic building blocks with useful inductive bias for many tasks.

Great progress has been made in automatic medical image segmentation due to powerful deep representation learning. The influence of transformer has led to research into its variants, and large-scale replacement of traditional CNN modules. However, such trend often overlooks the intrinsic feature extraction capabilities of the transformer and potential refinements to both the model and the transformer module through minor adjustments. This study proposes a novel deep medical image segmentation framework, called DA-TransUNet, aiming to introduce the Transformer and dual attention block into the encoder and decoder of the traditional U-shaped architecture. Unlike prior transformer-based solutions, our DA-TransUNet utilizes attention mechanism of transformer and multifaceted feature extraction of DA-Block, which can efficiently combine global, local, and multi-scale features to enhance medical image segmentation. Meanwhile, experimental results show that a dual attention block is added before the Transformer layer to facilitate feature extraction in the U-net structure. Furthermore, incorporating dual attention blocks in skip connections can enhance feature transfer to the decoder, thereby improving image segmentation performance. Experimental results across various benchmark of medical image segmentation reveal that DA-TransUNet significantly outperforms the state-of-the-art methods. The codes and parameters of our model will be publicly available at https://github.com/SUN-1024/DA-TransUnet.

Manifold learning flows are a class of generative modelling techniques that assume a low-dimensional manifold description of the data. The embedding of such manifold into the high-dimensional space of the data is achieved via learnable invertible transformations. Therefore, once the manifold is properly aligned via a reconstruction loss, the probability density is tractable on the manifold and maximum likelihood can be used optimize the network parameters. Naturally, the lower-dimensional representation of the data requires an injective-mapping. Recent approaches were able to enforce that density aligns with the modelled manifold, while efficiently calculating the density volume-change term when embedding to the higher-dimensional space. However, unless the injective-mapping is analytically predefined, the learned manifold is not necessarily an efficient representation of the data. Namely, the latent dimensions of such models frequently learn an entangled intrinsic basis with degenerate information being stored in each dimension. Alternatively, if a locally orthogonal and/or sparse basis is to be learned, here coined canonical intrinsic basis, it can serve in learning a more compact latent space representation. Towards this end, we propose a canonical manifold learning flow method, where a novel optimization objective enforces the transformation matrix to have few prominent and orthogonal basis functions. Canonical manifold flow yields a more efficient use of the latent space, automatically generating fewer prominent and distinct dimensions to represent data, and consequently a better approximation of target distributions than other manifold flow methods in most experiments we conducted, resulting in lower FID scores.

We propose the application of Transformer-based language models for classifying entity legal forms from raw legal entity names. Specifically, we employ various BERT variants and compare their performance against multiple traditional baselines. Our evaluation encompasses a substantial subset of freely available Legal Entity Identifier (LEI) data, comprising over 1.1 million legal entities from 30 different legal jurisdictions. The ground truth labels for classification per jurisdiction are taken from the Entity Legal Form (ELF) code standard (ISO 20275). Our findings demonstrate that pre-trained BERT variants outperform traditional text classification approaches in terms of F1 score, while also performing comparably well in the Macro F1 Score. Moreover, the validity of our proposal is supported by the outcome of third-party expert reviews conducted in ten selected jurisdictions. This study highlights the significant potential of Transformer-based models in advancing data standardization and data integration. The presented approaches can greatly benefit financial institutions, corporations, governments and other organizations in assessing business relationships, understanding risk exposure, and promoting effective governance.

We initiate a novel approach to explain the out of sample performance of random forest (RF) models by exploiting the fact that any RF can be formulated as an adaptive weighted K nearest-neighbors model. Specifically, we use the proximity between points in the feature space learned by the RF to re-write random forest predictions exactly as a weighted average of the target labels of training data points. This linearity facilitates a local notion of explainability of RF predictions that generates attributions for any model prediction across observations in the training set, and thereby complements established methods like SHAP, which instead generates attributions for a model prediction across dimensions of the feature space. We demonstrate this approach in the context of a bond pricing model trained on US corporate bond trades, and compare our approach to various existing approaches to model explainability.

This position paper explores the broad landscape of AI potentiality in the context of cybersecurity, with a particular emphasis on its possible risk factors with awareness, which can be managed by incorporating human experts in the loop, i.e., "Human-AI" teaming. As artificial intelligence (AI) technologies advance, they will provide unparalleled opportunities for attack identification, incident response, and recovery. However, the successful deployment of AI into cybersecurity measures necessitates an in-depth understanding of its capabilities, challenges, and ethical and legal implications to handle associated risk factors in real-world application areas. Towards this, we emphasize the importance of a balanced approach that incorporates AI's computational power with human expertise. AI systems may proactively discover vulnerabilities and detect anomalies through pattern recognition, and predictive modeling, significantly enhancing speed and accuracy. Human experts can explain AI-generated decisions to stakeholders, regulators, and end-users in critical situations, ensuring responsibility and accountability, which helps establish trust in AI-driven security solutions. Therefore, in this position paper, we argue that human-AI teaming is worthwhile in cybersecurity, in which human expertise such as intuition, critical thinking, or contextual understanding is combined with AI's computational power to improve overall cyber defenses.

Electric consumption prediction methods are investigated for many reasons such as decision-making related to energy efficiency as well as for anticipating demand in the energy market dynamics. The objective of the present work is the comparison between two Deep Learning models, namely the Long Short-Term Memory (LSTM) and Bi-directional LSTM (BLSTM) for univariate electric consumption Time Series (TS) short-term forecast. The Data Sets (DSs) were selected for their different contexts and scales, aiming the assessment of the models' robustness. Four DSs were used, related to the power consumption of: (a) a household in France; (b) a university building in Santar\'em, Brazil; (c) the T\'etouan city zones, in Morocco; and (c) the Singapore aggregated electric demand. The metrics RMSE, MAE, MAPE and R2 were calculated in a TS cross-validation scheme. The Friedman's test was applied to normalized RMSE (NRMSE) results, showing that BLSTM outperforms LSTM with statistically significant difference (p = 0.0455), corroborating the fact that bidirectional weight updating improves significantly the LSTM performance concerning different scales of electric power consumption.

Creating music is iterative, requiring varied methods at each stage. However, existing AI music systems fall short in orchestrating multiple subsystems for diverse needs. To address this gap, we introduce Loop Copilot, a novel system that enables users to generate and iteratively refine music through an interactive, multi-round dialogue interface. The system uses a large language model to interpret user intentions and select appropriate AI models for task execution. Each backend model is specialized for a specific task, and their outputs are aggregated to meet the user's requirements. To ensure musical coherence, essential attributes are maintained in a centralized table. We evaluate the effectiveness of the proposed system through semi-structured interviews and questionnaires, highlighting its utility not only in facilitating music creation but also its potential for broader applications.

This work reveals an evidential signal that emerges from the uncertainty value in Evidential Deep Learning (EDL). EDL is one example of a class of uncertainty-aware deep learning approaches designed to provide confidence (or epistemic uncertainty) about the current test sample. In particular for computer vision and bidirectional encoder large language models, the `evidential signal' arising from the Dirichlet strength in EDL can, in some cases, discriminate between classes, which is particularly strong when using large language models. We hypothesise that the KL regularisation term causes EDL to couple aleatoric and epistemic uncertainty. In this paper, we empirically investigate the correlations between misclassification and evaluated uncertainty, and show that EDL's `evidential signal' is due to misclassification bias. We critically evaluate EDL with other Dirichlet-based approaches, namely Generative Evidential Neural Networks (EDL-GEN) and Prior Networks, and show theoretically and empirically the differences between these loss functions. We conclude that EDL's coupling of uncertainty arises from these differences due to the use (or lack) of out-of-distribution samples during training.

Reasoning on knowledge graphs is a challenging task because it utilizes observed information to predict the missing one. Particularly, answering complex queries based on first-order logic is one of the crucial tasks to verify learning to reason abilities for generalization and composition. Recently, the prevailing method is query embedding which learns the embedding of a set of entities and treats logic operations as set operations and has shown great empirical success. Though there has been much research following the same formulation, many of its claims lack a formal and systematic inspection. In this paper, we rethink this formulation and justify many of the previous claims by characterizing the scope of queries investigated previously and precisely identifying the gap between its formulation and its goal, as well as providing complexity analysis for the currently investigated queries. Moreover, we develop a new dataset containing ten new types of queries with features that have never been considered and therefore can provide a thorough investigation of complex queries. Finally, we propose a new neural-symbolic method, Fuzzy Inference with Truth value (FIT), where we equip the neural link predictors with fuzzy logic theory to support end-to-end learning using complex queries with provable reasoning capability. Empirical results show that our method outperforms previous methods significantly in the new dataset and also surpasses previous methods in the existing dataset at the same time.

We present a Multimodal Interlaced Transformer (MIT) that jointly considers 2D and 3D data for weakly supervised point cloud segmentation. Research studies have shown that 2D and 3D features are complementary for point cloud segmentation. However, existing methods require extra 2D annotations to achieve 2D-3D information fusion. Considering the high annotation cost of point clouds, effective 2D and 3D feature fusion based on weakly supervised learning is in great demand. To this end, we propose a transformer model with two encoders and one decoder for weakly supervised point cloud segmentation using only scene-level class tags. Specifically, the two encoders compute the self-attended features for 3D point clouds and 2D multi-view images, respectively. The decoder implements interlaced 2D-3D cross-attention and carries out implicit 2D and 3D feature fusion. We alternately switch the roles of queries and key-value pairs in the decoder layers. It turns out that the 2D and 3D features are iteratively enriched by each other. Experiments show that it performs favorably against existing weakly supervised point cloud segmentation methods by a large margin on the S3DIS and ScanNet benchmarks. The project page will be available at https://jimmy15923.github.io/mit_web/.

Models trained on different datasets can be merged by a weighted-averaging of their parameters, but why does it work and when can it fail? Here, we connect the inaccuracy of weighted-averaging to mismatches in the gradients and propose a new uncertainty-based scheme to improve the performance by reducing the mismatch. The connection also reveals implicit assumptions in other schemes such as averaging, task arithmetic, and Fisher-weighted averaging. Our new method gives consistent improvements for large language models and vision transformers, both in terms of performance and robustness to hyperparameters.

Recent Language Models (LMs) have shown impressive capabilities in generating texts with the knowledge internalized in parameters. Yet, LMs often generate the factually incorrect responses to the given queries, since their knowledge may be inaccurate, incomplete, and outdated. To address this problem, previous works propose to augment LMs with the knowledge retrieved from an external knowledge source. However, such approaches often show suboptimal text generation performance due to two reasons: 1) the model may fail to retrieve the knowledge relevant to the given query, or 2) the model may not faithfully reflect the retrieved knowledge in the generated text. To overcome these, we propose to verify the output and the knowledge of the knowledge-augmented LMs with a separate verifier, which is a small LM that is trained to detect those two types of errors through instruction-finetuning. Then, when the verifier recognizes an error, we can rectify it by either retrieving new knowledge or generating new text. Further, we use an ensemble of the outputs from different instructions with a single verifier to enhance the reliability of the verification processes. We validate the effectiveness of the proposed verification steps on multiple question answering benchmarks, whose results show that the proposed verifier effectively identifies retrieval and generation errors, allowing LMs to provide more factually correct outputs. Our code is available at https://github.com/JinheonBaek/KALMV.

Open large language models (LLMs) with great performance in various tasks have significantly advanced the development of LLMs. However, they are far inferior to commercial models such as ChatGPT and GPT-4 when acting as agents to tackle complex tasks in the real world. These agent tasks employ LLMs as the central controller responsible for planning, memorization, and tool utilization, necessitating both fine-grained prompting methods and robust LLMs to achieve satisfactory performance. Though many prompting methods have been proposed to complete particular agent tasks, there is lack of research focusing on improving the agent capabilities of LLMs themselves without compromising their general abilities. In this work, we present AgentTuning, a simple and general method to enhance the agent abilities of LLMs while maintaining their general LLM capabilities. We construct AgentInstruct, a lightweight instruction-tuning dataset containing high-quality interaction trajectories. We employ a hybrid instruction-tuning strategy by combining AgentInstruct with open-source instructions from general domains. AgentTuning is used to instruction-tune the Llama 2 series, resulting in AgentLM. Our evaluations show that AgentTuning enables LLMs' agent capabilities without compromising general abilities. The AgentLM-70B is comparable to GPT-3.5-turbo on unseen agent tasks, demonstrating generalized agent capabilities. We open source the AgentInstruct and AgentLM-7B, 13B, and 70B models at https://github.com/THUDM/AgentTuning , serving open and powerful alternatives to commercial LLMs for agent tasks.

In light of the vulnerability of deep learning models to adversarial samples and the ensuing security issues, a range of methods, including Adversarial Training (AT) as a prominent representative, aimed at enhancing model robustness against various adversarial attacks, have seen rapid development. However, existing methods essentially assist the current state of target model to defend against parameter-oriented adversarial attacks with explicit or implicit computation burdens, which also suffers from unstable convergence behavior due to inconsistency of optimization trajectories. Diverging from previous work, this paper reconsiders the update rule of target model and corresponding deficiency to defend based on its current state. By introducing the historical state of the target model as a proxy, which is endowed with much prior information for defense, we formulate a two-stage update rule, resulting in a general adversarial defense framework, which we refer to as `LAST' ({\bf L}earn from the P{\bf ast}). Besides, we devise a Self Distillation (SD) based defense objective to constrain the update process of the proxy model without the introduction of larger teacher models. Experimentally, we demonstrate consistent and significant performance enhancements by refining a series of single-step and multi-step AT methods (e.g., up to $\bf 9.2\%$ and $\bf 20.5\%$ improvement of Robust Accuracy (RA) on CIFAR10 and CIFAR100 datasets, respectively) across various datasets, backbones and attack modalities, and validate its ability to enhance training stability and ameliorate catastrophic overfitting issues meanwhile.

Ocean science, which delves into the oceans that are reservoirs of life and biodiversity, is of great significance given that oceans cover over 70% of our planet's surface. Recently, advances in Large Language Models (LLMs) have transformed the paradigm in science. Despite the success in other domains, current LLMs often fall short in catering to the needs of domain experts like oceanographers, and the potential of LLMs for ocean science is under-explored. The intrinsic reason may be the immense and intricate nature of ocean data as well as the necessity for higher granularity and richness in knowledge. To alleviate these issues, we introduce OceanGPT, the first-ever LLM in the ocean domain, which is expert in various ocean science tasks. We propose DoInstruct, a novel framework to automatically obtain a large volume of ocean domain instruction data, which generates instructions based on multi-agent collaboration. Additionally, we construct the first oceanography benchmark, OceanBench, to evaluate the capabilities of LLMs in the ocean domain. Though comprehensive experiments, OceanGPT not only shows a higher level of knowledge expertise for oceans science tasks but also gains preliminary embodied intelligence capabilities in ocean technology. Codes, data and checkpoints will soon be available at https://github.com/zjunlp/KnowLM.

The key challenge underlying machine learning is generalisation to new data. This work studies generalisation for datasets consisting of related tasks that may differ in causal mechanisms. For example, observational medical data for complex diseases suffers from heterogeneity in causal mechanisms of disease across patients, creating challenges for machine learning algorithms that need to generalise to new patients outside of the training dataset. Common approaches for learning supervised models with heterogeneous datasets include learning a global model for the entire dataset, learning local models for each tasks' data, or utilising hierarchical, meta-learning and multi-task learning approaches to learn how to generalise from data pooled across multiple tasks. In this paper we propose causal similarity-based hierarchical Bayesian models to improve generalisation to new tasks by learning how to pool data from training tasks with similar causal mechanisms. We apply this general modelling principle to Bayesian neural networks and compare a variety of methods for estimating causal task similarity (for both known and unknown causal models). We demonstrate the benefits of our approach and applicability to real world problems through a range of experiments on simulated and real data.

In the realm of deep learning, transformers have emerged as a dominant architecture, particularly in natural language processing tasks. However, with their widespread adoption, concerns regarding the security and privacy of the data processed by these models have arisen. In this paper, we address a pivotal question: Can the data fed into transformers be recovered using their attention weights and outputs? We introduce a theoretical framework to tackle this problem. Specifically, we present an algorithm that aims to recover the input data $X \in \mathbb{R}^{d \times n}$ from given attention weights $W = QK^\top \in \mathbb{R}^{d \times d}$ and output $B \in \mathbb{R}^{n \times n}$ by minimizing the loss function $L(X)$. This loss function captures the discrepancy between the expected output and the actual output of the transformer. Our findings have significant implications for the Localized Layer-wise Mechanism (LLM), suggesting potential vulnerabilities in the model's design from a security and privacy perspective. This work underscores the importance of understanding and safeguarding the internal workings of transformers to ensure the confidentiality of processed data.

Many modern statistical analysis and machine learning applications require training models on sensitive user data. Differential privacy provides a formal guarantee that individual-level information about users does not leak. In this framework, randomized algorithms inject calibrated noise into the confidential data, resulting in privacy-protected datasets or queries. However, restricting access to only the privatized data during statistical analysis makes it computationally challenging to perform valid inferences on parameters underlying the confidential data. In this work, we propose simulation-based inference methods from privacy-protected datasets. Specifically, we use neural conditional density estimators as a flexible family of distributions to approximate the posterior distribution of model parameters given the observed private query results. We illustrate our methods on discrete time-series data under an infectious disease model and on ordinary linear regression models. Illustrating the privacy-utility trade-off, our experiments and analysis demonstrate the necessity and feasibility of designing valid statistical inference procedures to correct for biases introduced by the privacy-protection mechanisms.

Parameter-shared pre-trained language models (PLMs) have emerged as a successful approach in resource-constrained environments, enabling substantial reductions in model storage and memory costs without significant performance compromise. However, it is important to note that parameter sharing does not alleviate computational burdens associated with inference, thus impeding its practicality in situations characterized by limited stringent latency requirements or computational resources. Building upon neural ordinary differential equations (ODEs), we introduce a straightforward technique to enhance the inference efficiency of parameter-shared PLMs. Additionally, we propose a simple pre-training technique that leads to fully or partially shared models capable of achieving even greater inference acceleration. The experimental results demonstrate the effectiveness of our methods on both autoregressive and autoencoding PLMs, providing novel insights into more efficient utilization of parameter-shared models in resource-constrained settings.

Recent discoveries have revealed that deep neural networks might behave in a biased manner in many real-world scenarios. For instance, deep networks trained on a large-scale face recognition dataset CelebA tend to predict blonde hair for females and black hair for males. Such biases not only jeopardize the robustness of models but also perpetuate and amplify social biases, which is especially concerning for automated decision-making processes in healthcare, recruitment, etc., as they could exacerbate unfair economic and social inequalities among different groups. Existing debiasing methods suffer from high costs in bias labeling or model re-training, while also exhibiting a deficiency in terms of elucidating the origins of biases within the model. To this respect, we propose a fast model debiasing framework (FMD) which offers an efficient approach to identify, evaluate and remove biases inherent in trained models. The FMD identifies biased attributes through an explicit counterfactual concept and quantifies the influence of data samples with influence functions. Moreover, we design a machine unlearning-based strategy to efficiently and effectively remove the bias in a trained model with a small counterfactual dataset. Experiments on the Colored MNIST, CelebA, and Adult Income datasets along with experiments with large language models demonstrate that our method achieves superior or competing accuracies compared with state-of-the-art methods while attaining significantly fewer biases and requiring much less debiasing cost. Notably, our method requires only a small external dataset and updating a minimal amount of model parameters, without the requirement of access to training data that may be too large or unavailable in practice.

Metarounding is an approach to convert an approximation algorithm for linear optimization over some combinatorial classes to an online linear optimization algorithm for the same class. We propose a new metarounding algorithm under a natural assumption that a relax-based approximation algorithm exists for the combinatorial class. Our algorithm is much more efficient in both theoretical and practical aspects.

Prompt-based learning has shown its effectiveness in few-shot text classification. One important factor in its success is a verbalizer, which translates output from a language model into a predicted class. Notably, the simplest and widely acknowledged verbalizer employs manual labels to represent the classes. However, manual selection does not guarantee the optimality of the selected words when conditioned on the chosen language model. Therefore, we propose Label-Aware Automatic Verbalizer (LAAV), effectively augmenting the manual labels to achieve better few-shot classification results. Specifically, we use the manual labels along with the conjunction "and" to induce the model to generate more effective words for the verbalizer. The experimental results on five datasets across five languages demonstrate that LAAV significantly outperforms existing verbalizers. Furthermore, our analysis reveals that LAAV suggests more relevant words compared to similar approaches, especially in mid-to-low resource languages.

Medical time series data are indispensable in healthcare, providing critical insights for disease diagnosis, treatment planning, and patient management. The exponential growth in data complexity, driven by advanced sensor technologies, has presented challenges related to data labeling. Self-supervised learning (SSL) has emerged as a transformative approach to address these challenges, eliminating the need for extensive human annotation. In this study, we introduce a novel framework for Medical Time Series Representation Learning, known as MTS-LOF. MTS-LOF leverages the strengths of contrastive learning and Masked Autoencoder (MAE) methods, offering a unique approach to representation learning for medical time series data. By combining these techniques, MTS-LOF enhances the potential of healthcare applications by providing more sophisticated, context-rich representations. Additionally, MTS-LOF employs a multi-masking strategy to facilitate occlusion-invariant feature learning. This approach allows the model to create multiple views of the data by masking portions of it. By minimizing the discrepancy between the representations of these masked patches and the fully visible patches, MTS-LOF learns to capture rich contextual information within medical time series datasets. The results of experiments conducted on diverse medical time series datasets demonstrate the superiority of MTS-LOF over other methods. These findings hold promise for significantly enhancing healthcare applications by improving representation learning. Furthermore, our work delves into the integration of joint-embedding SSL and MAE techniques, shedding light on the intricate interplay between temporal and structural dependencies in healthcare data. This understanding is crucial, as it allows us to grasp the complexities of healthcare data analysis.

Neural operators, as an efficient surrogate model for learning the solutions of PDEs, have received extensive attention in the field of scientific machine learning. Among them, attention-based neural operators have become one of the mainstreams in related research. However, existing approaches overfit the limited training data due to the considerable number of parameters in the attention mechanism. To address this, we develop an orthogonal attention based on the eigendecomposition of the kernel integral operator and the neural approximation of eigenfunctions. The orthogonalization naturally poses a proper regularization effect on the resulting neural operator, which aids in resisting overfitting and boosting generalization. Experiments on six standard neural operator benchmark datasets comprising both regular and irregular geometries show that our method can outperform competing baselines with decent margins.

Large language models (LLMs) are susceptible to red teaming attacks, which can induce LLMs to generate harmful content. Previous research constructs attack prompts via manual or automatic methods, which have their own limitations on construction cost and quality. To address these issues, we propose an integrated approach that combines manual and automatic methods to economically generate high-quality attack prompts. Specifically, considering the impressive capabilities of newly emerged LLMs, we propose an attack framework to instruct LLMs to mimic human-generated prompts through in-context learning. Furthermore, we propose a defense framework that fine-tunes victim LLMs through iterative interactions with the attack framework to enhance their safety against red teaming attacks. Extensive experiments on different LLMs validate the effectiveness of our proposed attack and defense frameworks. Additionally, we release a series of attack prompts datasets named SAP with varying sizes, facilitating the safety evaluation and enhancement of more LLMs. Our code and dataset is available on https://github.com/Aatrox103/SAP .

Inference on modern Bayesian Neural Networks (BNNs) often relies on a variational inference treatment, imposing violated assumptions of independence and the form of the posterior. Traditional MCMC approaches avoid these assumptions at the cost of increased computation due to its incompatibility to subsampling of the likelihood. New Piecewise Deterministic Markov Process (PDMP) samplers permit subsampling, though introduce a model specific inhomogenous Poisson Process (IPPs) which is difficult to sample from. This work introduces a new generic and adaptive thinning scheme for sampling from these IPPs, and demonstrates how this approach can accelerate the application of PDMPs for inference in BNNs. Experimentation illustrates how inference with these methods is computationally feasible, can improve predictive accuracy, MCMC mixing performance, and provide informative uncertainty measurements when compared against other approximate inference schemes.

Clustering is an unsupervised machine learning methodology where unlabeled elements/objects are grouped together aiming to the construction of well-established clusters that their elements are classified according to their similarity. The goal of this process is to provide a useful aid to the researcher that will help her/him to identify patterns among the data. Dealing with large databases, such patterns may not be easily detectable without the contribution of a clustering algorithm. This article provides a deep description of the most widely used clustering methodologies accompanied by useful presentations concerning suitable parameter selection and initializations. Simultaneously, this article not only represents a review highlighting the major elements of examined clustering techniques but emphasizes the comparison of these algorithms' clustering efficiency based on 3 datasets, revealing their existing weaknesses and capabilities through accuracy and complexity, during the confrontation of discrete and continuous observations. The produced results help us extract valuable conclusions about the appropriateness of the examined clustering techniques in accordance with the dataset's size.

Existing works have made great progress in improving adversarial robustness, but typically test their method only on data from the same distribution as the training data, i.e. in-distribution (ID) testing. As a result, it is unclear how such robustness generalizes under input distribution shifts, i.e. out-of-distribution (OOD) testing. This is a concerning omission as such distribution shifts are unavoidable when methods are deployed in the wild. To address this issue we propose a benchmark named OODRobustBench to comprehensively assess OOD adversarial robustness using 23 dataset-wise shifts (i.e. naturalistic shifts in input distribution) and 6 threat-wise shifts (i.e., unforeseen adversarial threat models). OODRobustBench is used to assess 706 robust models using 60.7K adversarial evaluations. This large-scale analysis shows that: 1) adversarial robustness suffers from a severe OOD generalization issue; 2) ID robustness correlates strongly with OOD robustness, in a positive linear way, under many distribution shifts. The latter enables the prediction of OOD robustness from ID robustness. Based on this, we are able to predict the upper limit of OOD robustness for existing robust training schemes. The results suggest that achieving OOD robustness requires designing novel methods beyond the conventional ones. Last, we discover that extra data, data augmentation, advanced model architectures and particular regularization approaches can improve OOD robustness. Noticeably, the discovered training schemes, compared to the baseline, exhibit dramatically higher robustness under threat shift while keeping high ID robustness, demonstrating new promising solutions for robustness against both multi-attack and unforeseen attacks.

Recently there has been a lot of interest in non-autoregressive (non-AR) models for speech synthesis, such as FastSpeech 2 and diffusion models. Unlike AR models, these models do not have autoregressive dependencies among outputs which makes inference efficient. This paper expands the range of available non-AR models with another member called energy-based models (EBMs). The paper describes how noise contrastive estimation, which relies on the comparison between positive and negative samples, can be used to train EBMs. It proposes a number of strategies for generating effective negative samples, including using high-performing AR models. It also describes how sampling from EBMs can be performed using Langevin Markov Chain Monte-Carlo (MCMC). The use of Langevin MCMC enables to draw connections between EBMs and currently popular diffusion models. Experiments on LJSpeech dataset show that the proposed approach offers improvements over Tacotron 2.

Machine learning has rapidly become a tool of choice for the astronomical community. It is being applied across a wide range of wavelengths and problems, from the classification of transients to neural network emulators of cosmological simulations, and is shifting paradigms about how we generate and report scientific results. At the same time, this class of method comes with its own set of best practices, challenges, and drawbacks, which, at present, are often reported on incompletely in the astrophysical literature. With this paper, we aim to provide a primer to the astronomical community, including authors, reviewers, and editors, on how to implement machine learning models and report their results in a way that ensures the accuracy of the results, reproducibility of the findings, and usefulness of the method.

Despite the known limitations, most machine translation systems today still operate on the sentence-level. One reason for this is, that most parallel training data is only sentence-level aligned, without document-level meta information available. In this work, we set out to build context-aware translation systems utilizing document-level monolingual data instead. This can be achieved by combining any existing sentence-level translation model with a document-level language model. We improve existing approaches by leveraging recent advancements in model combination. Additionally, we propose novel weighting techniques that make the system combination more flexible and significantly reduce computational overhead. In a comprehensive evaluation on four diverse translation tasks, we show that our extensions improve document-targeted scores substantially and are also computationally more efficient. However, we also find that in most scenarios, back-translation gives even better results, at the cost of having to re-train the translation system. Finally, we explore language model fusion in the light of recent advancements in large language models. Our findings suggest that there might be strong potential in utilizing large language models via model combination.  ( 2 min )

SCGAN adds a similarity constraint between generated images and conditions as a regularization term on generative adversarial networks. Similarity constraint works as a tutor to instruct the generator network to comprehend the difference of representations based on conditions. We understand how SCGAN works on a deeper level. This understanding makes us realize that the similarity constraint functions like the contrastive loss function. We believe that a model with high understanding and intelligence measures the similarity between images based on their structure and high level features, just like humans do. Two major changes we applied to SCGAN in order to make a modified model are using SSIM to measure similarity between images and applying contrastive loss principles to the similarity constraint. The modified model performs better using FID and FactorVAE metrics. The modified model also has better generalisability compared to other models. Keywords Generative Adversarial Nets, Unsupervised Learning, Disentangled Representation Learning, Contrastive Disentanglement, SSIM  ( 2 min )

Options, serving as a crucial financial instrument, are used by investors to manage and mitigate their investment risks within the securities market. Precisely predicting the present price of an option enables investors to make informed and efficient decisions. In this paper, we propose a machine learning method for forecasting the prices of SPY (ETF) option based on gated recurrent unit (GRU) and self-attention mechanism. We first partitioned the raw dataset into 15 subsets according to moneyness and days to maturity criteria. For each subset, we matched the corresponding U.S. government bond rates and Implied Volatility Indices. This segmentation allows for a more insightful exploration of the impacts of risk-free rates and underlying volatility on option pricing. Next, we built four different machine learning models, including multilayer perceptron (MLP), long short-term memory (LSTM), self-attention LSTM, and self-attention GRU in comparison to the traditional binomial model. The empirical result shows that self-attention GRU with historical data outperforms other models due to its ability to capture complex temporal dependencies and leverage the contextual information embedded in the historical data. Finally, in order to unveil the "black box" of artificial intelligence, we employed the SHapley Additive exPlanations (SHAP) method to interpret and analyze the prediction results of the self-attention GRU model with historical data. This provides insights into the significance and contributions of different input features on the pricing of American-style options.  ( 2 min )

Randomized experimental comparisons of alternative pedagogical strategies could provide useful empirical evidence in instructors' decision-making. However, traditional experiments do not have a clear and simple pathway to using data rapidly to try to increase the chances that students in an experiment get the best conditions. Drawing inspiration from the use of machine learning and experimentation in product development at leading technology companies, we explore how adaptive experimentation might help in continuous course improvement. In adaptive experiments, as different arms/conditions are deployed to students, data is analyzed and used to change the experience for future students. This can be done using machine learning algorithms to identify which actions are more promising for improving student experience or outcomes. This algorithm can then dynamically deploy the most effective conditions to future students, resulting in better support for students' needs. We illustrate the approach with a case study providing a side-by-side comparison of traditional and adaptive experimentation of self-explanation prompts in online homework problems in a CS1 course. This provides a first step in exploring the future of how this methodology can be useful in bridging research and practice in doing continuous improvement.  ( 2 min )

Matching, a task to optimally assign limited resources under constraints, is a fundamental technology for society. The task potentially has various objectives, conditions, and constraints; however, the efficient neural network architecture for matching is underexplored. This paper proposes a novel graph neural network (GNN), \textit{WeaveNet}, designed for bipartite graphs. Since a bipartite graph is generally dense, general GNN architectures lose node-wise information by over-smoothing when deeply stacked. Such a phenomenon is undesirable for solving matching problems. WeaveNet avoids it by preserving edge-wise information while passing messages densely to reach a better solution. To evaluate the model, we approximated one of the \textit{strongly NP-hard} problems, \textit{fair stable matching}. Despite its inherent difficulties and the network's general purpose design, our model reached a comparative performance with state-of-the-art algorithms specially designed for stable matching for small numbers of agents.  ( 2 min )

Graph Neural Networks (GNNs) have shown remarkable merit in performing various learning-based tasks in complex networks. The superior performance of GNNs often correlates with the availability and quality of node-level features in the input networks. However, for many network applications, such node-level information may be missing or unreliable, thereby limiting the applicability and efficacy of GNNs. To address this limitation, we present a novel approach denoted as Ego-centric Spectral subGraph Embedding Augmentation (ESGEA), which aims to enhance and design node features, particularly in scenarios where information is lacking. Our method leverages the topological structure of the local subgraph to create topology-aware node features. The subgraph features are generated using an efficient spectral graph embedding technique, and they serve as node features that capture the local topological organization of the network. The explicit node features, if present, are then enhanced with the subgraph embeddings in order to improve the overall performance. ESGEA is compatible with any GNN-based architecture and is effective even in the absence of node features. We evaluate the proposed method in a social network graph classification task where node attributes are unavailable, as well as in a node classification task where node features are corrupted or even absent. The evaluation results on seven datasets and eight baseline models indicate up to a 10% improvement in AUC and a 7% improvement in accuracy for graph and node classification tasks, respectively.  ( 3 min )

Graph Neural Networks (GNNs) have been widely used for various types of graph data processing and analytical tasks in different domains. Training GNNs over centralized graph data can be infeasible due to privacy concerns and regulatory restrictions. Thus, federated learning (FL) becomes a trending solution to address this challenge in a distributed learning paradigm. However, as GNNs may inherit historical bias from training data and lead to discriminatory predictions, the bias of local models can be easily propagated to the global model in distributed settings. This poses a new challenge in mitigating bias in federated GNNs. To address this challenge, we propose $\text{F}^2$GNN, a Fair Federated Graph Neural Network, that enhances group fairness of federated GNNs. As bias can be sourced from both data and learning algorithms, $\text{F}^2$GNN aims to mitigate both types of bias under federated settings. First, we provide theoretical insights on the connection between data bias in a training graph and statistical fairness metrics of the trained GNN models. Based on the theoretical analysis, we design $\text{F}^2$GNN which contains two key components: a fairness-aware local model update scheme that enhances group fairness of the local models on the client side, and a fairness-weighted global model update scheme that takes both data bias and fairness metrics of local models into consideration in the aggregation process. We evaluate $\text{F}^2$GNN empirically versus a number of baseline methods, and demonstrate that $\text{F}^2$GNN outperforms these baselines in terms of both fairness and model accuracy.  ( 3 min )

Consider the following problem: given a few demonstrations of a task across a few different objects, how can a robot learn to perform that same task on new, previously unseen objects? This is challenging because the large variety of objects within a class makes it difficult to infer the task-relevant relationship between the new objects and the objects in the demonstrations. We address this by formulating imitation learning as a conditional alignment problem between graph representations of objects. Consequently, we show that this conditioning allows for in-context learning, where a robot can perform a task on a set of new objects immediately after the demonstrations, without any prior knowledge about the object class or any further training. In our experiments, we explore and validate our design choices, and we show that our method is highly effective for few-shot learning of several real-world, everyday tasks, whilst outperforming baselines. Videos are available on our project webpage at https://www.robot-learning.uk/implicit-graph-alignment.  ( 2 min )

Deep Learning models have shown remarkable performance in a broad range of vision tasks. However, they are often vulnerable against domain shifts at test-time. Test-time training (TTT) methods have been developed in an attempt to mitigate these vulnerabilities, where a secondary task is solved at training time simultaneously with the main task, to be later used as an self-supervised proxy task at test-time. In this work, we propose a novel unsupervised TTT technique based on the maximization of Mutual Information between multi-scale feature maps and a discrete latent representation, which can be integrated to the standard training as an auxiliary clustering task. Experimental results demonstrate competitive classification performance on different popular test-time adaptation benchmarks.  ( 2 min )

The optimal placement of sensors for environmental monitoring and disaster management is a challenging problem due to its NP-hard nature. Traditional methods for sensor placement involve exact, approximation, or heuristic approaches, with the latter being the most widely used. However, heuristic methods are limited by expert intuition and experience. Deep learning (DL) has emerged as a promising approach for generating heuristic algorithms automatically. In this paper, we introduce a novel sensor placement approach focused on learning improvement heuristics using deep reinforcement learning (RL) methods. Our approach leverages an RL formulation for learning improvement heuristics, driven by an actor-critic algorithm for training the policy network. We compare our method with several state-of-the-art approaches by conducting comprehensive experiments, demonstrating the effectiveness and superiority of our proposed approach in producing high-quality solutions. Our work presents a promising direction for applying advanced DL and RL techniques to challenging climate sensor placement problems.  ( 2 min )

In recent years, Massive Open Online Courses (MOOCs) have gained significant traction as a rapidly growing phenomenon in online learning. Unlike traditional classrooms, MOOCs offer a unique opportunity to cater to a diverse audience from different backgrounds and geographical locations. Renowned universities and MOOC-specific providers, such as Coursera, offer MOOC courses on various subjects. Automated assessment tasks like grade and early dropout predictions are necessary due to the high enrollment and limited direct interaction between teachers and learners. However, current automated assessment approaches overlook the structural links between different entities involved in the downstream tasks, such as the students and courses. Our hypothesis suggests that these structural relationships, manifested through an interaction graph, contain valuable information that can enhance the performance of the task at hand. To validate this, we construct a unique knowledge graph for a large MOOC dataset, which will be publicly available to the research community. Furthermore, we utilize graph embedding techniques to extract latent structural information encoded in the interactions between entities in the dataset. These techniques do not require ground truth labels and can be utilized for various tasks. Finally, by combining entity-specific features, behavioral features, and extracted structural features, we enhance the performance of predictive machine learning models in student assignment grade prediction. Our experiments demonstrate that structural features can significantly improve the predictive performance of downstream assessment tasks. The code and data are available in \url{https://github.com/DSAatUSU/MOOPer_grade_prediction}  ( 3 min )

Variable speed limit (VSL) control is a promising traffic management strategy for enhancing safety and mobility. This work introduces MARVEL, a multi-agent reinforcement learning (MARL) framework for implementing large-scale VSL control on freeway corridors using only commonly available data. The agents learn through a reward structure that incorporates adaptability to traffic conditions, safety, and mobility; enabling coordination among the agents. The proposed framework scales to cover corridors with many gantries thanks to a parameter sharing among all VSL agents. The agents are trained in a microsimulation environment based on a short freeway stretch with 8 gantries spanning 7 miles and tested with 34 gantries spanning 17 miles of I-24 near Nashville, TN. MARVEL improves traffic safety by 63.4% compared to the no control scenario and enhances traffic mobility by 14.6% compared to a state-of-the-practice algorithm that has been deployed on I-24. An explainability analysis is undertaken to explore the learned policy under different traffic conditions and the results provide insights into the decision-making process of agents. Finally, we test the policy learned from the simulation-based experiments on real input data from I-24 to illustrate the potential deployment capability of the learned policy.  ( 2 min )

Graph neural networks (GNNs) have gained significant popularity due to the powerful capability to extract useful representations from graph data. As the need for efficient GNN computation intensifies, a variety of programming abstractions designed for optimizing GNN Aggregation have emerged to facilitate acceleration. However, there is no comprehensive evaluation and analysis upon existing abstractions, thus no clear consensus on which approach is better. In this letter, we classify existing programming abstractions for GNN Aggregation by the dimension of data organization and propagation method. By constructing these abstractions on a state-of-the-art GNN library, we perform a thorough and detailed characterization study to compare their performance and efficiency, and provide several insights on future GNN acceleration based on our analysis.  ( 2 min )

The performance of neural networks has been significantly improved by increasing the number of channels in convolutional layers. However, this increase in performance comes with a higher computational cost, resulting in numerous studies focused on reducing it. One promising approach to address this issue is group convolution, which effectively reduces the computational cost by grouping channels. However, to the best of our knowledge, there has been no theoretical analysis on how well the group convolution approximates the standard convolution. In this paper, we mathematically analyze the approximation of the group convolution to the standard convolution with respect to the number of groups. Furthermore, we propose a novel variant of the group convolution called balanced group convolution, which shows a higher approximation with a small additional computational cost. We provide experimental results that validate our theoretical findings and demonstrate the superior performance of the balanced group convolution over other variants of group convolution.  ( 2 min )

Recently, the field of machine learning has undergone a transition from model-centric to data-centric. The advancements in diverse learning tasks have been propelled by the accumulation of more extensive datasets, subsequently facilitating the training of larger models on these datasets. However, these datasets remain relatively under-explored. To this end, we introduce a pioneering approach known as RK-core, to empower gaining a deeper understanding of the intricate hierarchical structure within datasets. Across several benchmark datasets, we find that samples with low coreness values appear less representative of their respective categories, and conversely, those with high coreness values exhibit greater representativeness. Correspondingly, samples with high coreness values make a more substantial contribution to the performance in comparison to those with low coreness values. Building upon this, we further employ RK-core to analyze the hierarchical structure of samples with different coreset selection methods. Remarkably, we find that a high-quality coreset should exhibit hierarchical diversity instead of solely opting for representative samples. The code is available at https://github.com/yaolu-zjut/Kcore.  ( 2 min )

Numerous methods for time series anomaly detection (TSAD) methods have emerged in recent years. Most existing methods are unsupervised and assume the availability of normal training samples only, while few supervised methods have shown superior performance by incorporating labeled anomalous samples in the training phase. However, certain anomaly types are inherently challenging for unsupervised methods to differentiate from normal data, while supervised methods are constrained to detecting anomalies resembling those present during training, failing to generalize to unseen anomaly classes. This paper is the first attempt in providing a novel approach for the open-set TSAD problem, in which a small number of labeled anomalies from a limited class of anomalies are visible in the training phase, with the objective of detecting both seen and unseen anomaly classes in the test phase. The proposed method, called Multivariate Open-Set timeseries Anomaly Detection (MOSAD) consists of three primary modules: a Feature Extractor to extract meaningful time-series features; a Multi-head Network consisting of Generative-, Deviation-, and Contrastive heads for capturing both seen and unseen anomaly classes; and an Anomaly Scoring module leveraging the insights of the three heads to detect anomalies. Extensive experiments on three real-world datasets consistently show that our approach surpasses existing methods under various experimental settings, thus establishing a new state-of-the-art performance in the TSAD field.  ( 2 min )

Molecular language modeling is an effective approach to generating novel chemical structures. However, these models do not \emph{a priori} encode certain preferences a chemist may desire. We investigate the use of fine-tuning using Direct Preference Optimization to better align generated molecules with chemist preferences. Our findings suggest that this approach is simple, efficient, and highly effective.  ( 2 min )

With evolving data regulations, machine unlearning (MU) has become an important tool for fostering trust and safety in today's AI models. However, existing MU methods focusing on data and/or weight perspectives often grapple with limitations in unlearning accuracy, stability, and cross-domain applicability. To address these challenges, we introduce the concept of 'weight saliency' in MU, drawing parallels with input saliency in model explanation. This innovation directs MU's attention toward specific model weights rather than the entire model, improving effectiveness and efficiency. The resultant method that we call saliency unlearning (SalUn) narrows the performance gap with 'exact' unlearning (model retraining from scratch after removing the forgetting dataset). To the best of our knowledge, SalUn is the first principled MU approach adaptable enough to effectively erase the influence of forgetting data, classes, or concepts in both image classification and generation. For example, SalUn yields a stability advantage in high-variance random data forgetting, e.g., with a 0.2% gap compared to exact unlearning on the CIFAR-10 dataset. Moreover, in preventing conditional diffusion models from generating harmful images, SalUn achieves nearly 100% unlearning accuracy, outperforming current state-of-the-art baselines like Erased Stable Diffusion and Forget-Me-Not.  ( 2 min )

Understanding the long-term impact of algorithmic interventions on society is vital to achieving responsible AI. Traditional evaluation strategies often fall short due to the complex, adaptive and dynamic nature of society. While reinforcement learning (RL) can be a powerful approach for optimizing decisions in dynamic settings, the difficulty of realistic environment design remains a barrier to building robust agents that perform well in practical settings. To address this issue we tap into the field of system dynamics (SD) as a complementary method that incorporates collaborative simulation model specification practices. We introduce SDGym, a low-code library built on the OpenAI Gym framework which enables the generation of custom RL environments based on SD simulation models. Through a feasibility study we validate that well specified, rich RL environments can be generated from preexisting SD models and a few lines of configuration code. We demonstrate the capabilities of the SDGym environment using an SD model of the electric vehicle adoption problem. We compare two SD simulators, PySD and BPTK-Py for parity, and train a D4PG agent using the Acme framework to showcase learning and environment interaction. Our preliminary findings underscore the dual potential of SD to improve RL environment design and for RL to improve dynamic policy discovery within SD models. By open-sourcing SDGym, the intent is to galvanize further research and promote adoption across the SD and RL communities, thereby catalyzing collaboration in this emerging interdisciplinary space.  ( 2 min )

Despite their better convergence properties compared to first-order optimizers, second-order optimizers for deep learning have been less popular due to their significant computational costs. The primary efficiency bottleneck in such optimizers is matrix inverse calculations in the preconditioning step, which are expensive to compute on GPUs. In this paper, we introduce Jorge, a second-order optimizer that promises the best of both worlds -- rapid convergence benefits of second-order methods, and high computational efficiency typical of first-order methods. We address the primary computational bottleneck of computing matrix inverses by completely eliminating them using an approximation of the preconditioner computation. This makes Jorge extremely efficient on GPUs in terms of wall-clock time. Further, we describe an approach to determine Jorge's hyperparameters directly from a well-tuned SGD baseline, thereby significantly minimizing tuning efforts. Our empirical evaluations demonstrate the distinct advantages of using Jorge, outperforming state-of-the-art optimizers such as SGD, AdamW, and Shampoo across multiple deep learning models, both in terms of sample efficiency and wall-clock time.  ( 2 min )

Driving safety is a top priority for autonomous vehicles. Orthogonal to prior work handling accident-prone traffic events by algorithm designs at the policy level, we investigate a Closed-loop Adversarial Training (CAT) framework for safe end-to-end driving in this paper through the lens of environment augmentation. CAT aims to continuously improve the safety of driving agents by training the agent on safety-critical scenarios that are dynamically generated over time. A novel resampling technique is developed to turn log-replay real-world driving scenarios into safety-critical ones via probabilistic factorization, where the adversarial traffic generation is modeled as the multiplication of standard motion prediction sub-problems. Consequently, CAT can launch more efficient physical attacks compared to existing safety-critical scenario generation methods and yields a significantly less computational cost in the iterative learning pipeline. We incorporate CAT into the MetaDrive simulator and validate our approach on hundreds of driving scenarios imported from real-world driving datasets. Experimental results demonstrate that CAT can effectively generate adversarial scenarios countering the agent being trained. After training, the agent can achieve superior driving safety in both log-replay and safety-critical traffic scenarios on the held-out test set. Code and data are available at https://metadriverse.github.io/cat.  ( 2 min )

Supervised contrastive loss (SCL) is a competitive and often superior alternative to the cross-entropy loss for classification. While prior studies have demonstrated that both losses yield symmetric training representations under balanced data, this symmetry breaks under class imbalances. This paper presents an intriguing discovery: the introduction of a ReLU activation at the final layer effectively restores the symmetry in SCL-learned representations. We arrive at this finding analytically, by establishing that the global minimizers of an unconstrained features model with SCL loss and entry-wise non-negativity constraints form an orthogonal frame. Extensive experiments conducted across various datasets, architectures, and imbalance scenarios corroborate our finding. Importantly, our experiments reveal that the inclusion of the ReLU activation restores symmetry without compromising test accuracy. This constitutes the first geometry characterization of SCL under imbalances. Additionally, our analysis and experiments underscore the pivotal role of batch selection strategies in representation geometry. By proving necessary and sufficient conditions for mini-batch choices that ensure invariant symmetric representations, we introduce batch-binding as an efficient strategy that guarantees these conditions hold.  ( 2 min )

The dynamical formulation of the optimal transport can be extended through various choices of the underlying geometry ($\textit{kinetic energy}$), and the regularization of density paths ($\textit{potential energy}$). These combinations yield different variational problems ($\textit{Lagrangians}$), encompassing many variations of the optimal transport problem such as the Schr\"odinger bridge, unbalanced optimal transport, and optimal transport with physical constraints, among others. In general, the optimal density path is unknown, and solving these variational problems can be computationally challenging. Leveraging the dual formulation of the Lagrangians, we propose a novel deep learning based framework approaching all of these problems from a unified perspective. Our method does not require simulating or backpropagating through the trajectories of the learned dynamics, and does not need access to optimal couplings. We showcase the versatility of the proposed framework by outperforming previous approaches for the single-cell trajectory inference, where incorporating prior knowledge into the dynamics is crucial for correct predictions.  ( 2 min )

Clustering is an unsupervised machine learning methodology where unlabeled elements/objects are grouped together aiming to the construction of well-established clusters that their elements are classified according to their similarity. The goal of this process is to provide a useful aid to the researcher that will help her/him to identify patterns among the data. Dealing with large databases, such patterns may not be easily detectable without the contribution of a clustering algorithm. This article provides a deep description of the most widely used clustering methodologies accompanied by useful presentations concerning suitable parameter selection and initializations. Simultaneously, this article not only represents a review highlighting the major elements of examined clustering techniques but emphasizes the comparison of these algorithms' clustering efficiency based on 3 datasets, revealing their existing weaknesses and capabilities through accuracy and complexity, during the confrontation of discrete and continuous observations. The produced results help us extract valuable conclusions about the appropriateness of the examined clustering techniques in accordance with the dataset's size.  ( 3 min )

On-device machine learning (ML) enables the training process to exploit a massive amount of user-generated private data samples. To enjoy this benefit, inter-device communication overhead should be minimized. With this end, we propose federated distillation (FD), a distributed model training algorithm whose communication payload size is much smaller than a benchmark scheme, federated learning (FL), particularly when the model size is large. Moreover, user-generated data samples are likely to become non-IID across devices, which commonly degrades the performance compared to the case with an IID dataset. To cope with this, we propose federated augmentation (FAug), where each device collectively trains a generative model, and thereby augments its local data towards yielding an IID dataset. Empirical studies demonstrate that FD with FAug yields around 26x less communication overhead while achieving 95-98% test accuracy compared to FL.  ( 2 min )

Partially observable Markov decision processes (POMDPs) provide a flexible representation for real-world decision and control problems. However, POMDPs are notoriously difficult to solve, especially when the state and observation spaces are continuous or hybrid, which is often the case for physical systems. While recent online sampling-based POMDP algorithms that plan with observation likelihood weighting have shown practical effectiveness, a general theory characterizing the approximation error of the particle filtering techniques that these algorithms use has not previously been proposed. Our main contribution is bounding the error between any POMDP and its corresponding finite sample particle belief MDP (PB-MDP) approximation. This fundamental bridge between PB-MDPs and POMDPs allows us to adapt any sampling-based MDP algorithm to a POMDP by solving the corresponding particle belief MDP, thereby extending the convergence guarantees of the MDP algorithm to the POMDP. Practically, this is implemented by using the particle filter belief transition model as the generative model for the MDP solver. While this requires access to the observation density model from the POMDP, it only increases the transition sampling complexity of the MDP solver by a factor of $\mathcal{O}(C)$, where $C$ is the number of particles. Thus, when combined with sparse sampling MDP algorithms, this approach can yield algorithms for POMDPs that have no direct theoretical dependence on the size of the state and observation spaces. In addition to our theoretical contribution, we perform five numerical experiments on benchmark POMDPs to demonstrate that a simple MDP algorithm adapted using PB-MDP approximation, Sparse-PFT, achieves performance competitive with other leading continuous observation POMDP solvers.  ( 3 min )

We present a novel variational framework for performing inference in (neural) stochastic differential equations (SDEs) driven by Markov-approximate fractional Brownian motion (fBM). SDEs offer a versatile tool for modeling real-world continuous-time dynamic systems with inherent noise and randomness. Combining SDEs with the powerful inference capabilities of variational methods, enables the learning of representative function distributions through stochastic gradient descent. However, conventional SDEs typically assume the underlying noise to follow a Brownian motion (BM), which hinders their ability to capture long-term dependencies. In contrast, fractional Brownian motion (fBM) extends BM to encompass non-Markovian dynamics, but existing methods for inferring fBM parameters are either computationally demanding or statistically inefficient. In this paper, building upon the Markov approximation of fBM, we derive the evidence lower bound essential for efficient variational inference of posterior path measures, drawing from the well-established field of stochastic analysis. Additionally, we provide a closed-form expression to determine optimal approximation coefficients. Furthermore, we propose the use of neural networks to learn the drift, diffusion and control terms within our variational posterior, leading to the variational training of neural-SDEs. In this framework, we also optimize the Hurst index, governing the nature of our fractional noise. Beyond validation on synthetic data, we contribute a novel architecture for variational latent video prediction,-an approach that, to the best of our knowledge, enables the first variational neural-SDE application to video perception.  ( 3 min )

Feature preprocessing continues to play a critical role when applying machine learning and statistical methods to tabular data. In this paper, we propose the use of the kernel density integral transformation as a feature preprocessing step. Our approach subsumes the two leading feature preprocessing methods as limiting cases: linear min-max scaling and quantile transformation. We demonstrate that, without hyperparameter tuning, the kernel density integral transformation can be used as a simple drop-in replacement for either method, offering protection from the weaknesses of each. Alternatively, with tuning of a single continuous hyperparameter, we frequently outperform both of these methods. Finally, we show that the kernel density transformation can be profitably applied to statistical data analysis, particularly in correlation analysis and univariate clustering.  ( 2 min )

Prediction sets capture uncertainty by predicting sets of labels rather than individual labels, enabling downstream decisions to conservatively account for all plausible outcomes. Conformal inference algorithms construct prediction sets guaranteed to contain the true label with high probability. These guarantees fail to hold in the face of distribution shift, which is precisely when reliable uncertainty quantification can be most useful. We propose a novel algorithm for constructing prediction sets with PAC guarantees in the label shift setting. This method estimates the predicted probabilities of the classes in a target domain, as well as the confusion matrix, then propagates uncertainty in these estimates through a Gaussian elimination algorithm to compute confidence intervals for importance weights. Finally, it uses these intervals to construct prediction sets. We evaluate our approach on five datasets: the CIFAR-10, ChestX-Ray and Entity-13 image datasets, the tabular CDC Heart dataset, and the AGNews text dataset. Our algorithm satisfies the PAC guarantee while producing smaller, more informative, prediction sets compared to several baselines.  ( 2 min )

This paper introduces a new regularized version of the robust $\tau$-regression estimator for analyzing high-dimensional datasets subject to gross contamination in the response variables and covariates (explanatory variables). The resulting estimator, termed adaptive $\tau$-Lasso, is robust to outliers and high-leverage points. It also incorporates an adaptive $\ell_1$-norm penalty term, which enables the selection of relevant variables and reduces the bias associated with large true regression coefficients. More specifically, this adaptive $\ell_1$-norm penalty term assigns a weight to each regression coefficient. For a fixed number of predictors $p$, we show that the adaptive $\tau$-Lasso has the oracle property, ensuring both variable-selection consistency and asymptotic normality. Asymptotic normality applies only to the entries of the regression vector corresponding to the true support, assuming knowledge of the true regression vector support. We characterize its robustness via the finite-sample breakdown point and the influence function. We carry out extensive simulations and observe that the class of $\tau$-Lasso estimators exhibits robustness and reliable performance in both contaminated and uncontaminated data settings. We also validate our theoretical findings on robustness properties through simulation experiments. In the face of outliers and high-leverage points, the adaptive $\tau$-Lasso and $\tau$-Lasso estimators achieve the best performance or close-to-best performance in terms of prediction and variable selection accuracy compared to other competing regularized estimators for all scenarios considered in this study. Therefore, the adaptive $\tau$-Lasso and $\tau$-Lasso estimators can be effectively employed for a variety of sparse linear regression problems, particularly in high-dimensional settings and when the data is contaminated by outliers and high-leverage points.  ( 3 min )

Representation learning has significantly driven the field to develop pretrained models that can act as a valuable starting point when transferring to new datasets. With the rising demand for reliable machine learning and uncertainty quantification, there is a need for pretrained models that not only provide embeddings but also transferable uncertainty estimates. To guide the development of such models, we propose the Uncertainty-aware Representation Learning (URL) benchmark. Besides the transferability of the representations, it also measures the zero-shot transferability of the uncertainty estimate using a novel metric. We apply URL to evaluate eleven uncertainty quantifiers that are pretrained on ImageNet and transferred to eight downstream datasets. We find that approaches that focus on the uncertainty of the representation itself or estimate the prediction risk directly outperform those that are based on the probabilities of upstream classes. Yet, achieving transferable uncertainty quantification remains an open challenge. Our findings indicate that it is not necessarily in conflict with traditional representation learning goals. Code is provided under https://github.com/mkirchhof/url .  ( 2 min )

Gibbs posteriors are proportional to a prior distribution multiplied by an exponentiated loss function, with a key tuning parameter weighting information in the loss relative to the prior and providing a control of posterior uncertainty. Gibbs posteriors provide a principled framework for likelihood-free Bayesian inference, but in many situations, including a single tuning parameter inevitably leads to poor uncertainty quantification. In particular, regardless of the value of the parameter, credible regions have far from the nominal frequentist coverage even in large samples. We propose a sequential extension to Gibbs posteriors to address this problem. We prove the proposed sequential posterior exhibits concentration and a Bernstein-von Mises theorem, which holds under easy to verify conditions in Euclidean space and on manifolds. As a byproduct, we obtain the first Bernstein-von Mises theorem for traditional likelihood-based Bayesian posteriors on manifolds. All methods are illustrated with an application to principal component analysis.  ( 2 min )

This work introduces the first toolkit around path-norms that is fully able to encompass general DAG ReLU networks with biases, skip connections and any operation based on the extraction of order statistics: max pooling, GroupSort etc. This toolkit notably allows us to establish generalization bounds for modern neural networks that are not only the most widely applicable path-norm based ones, but also recover or beat the sharpest known bounds of this type. These extended path-norms further enjoy the usual benefits of path-norms: ease of computation, invariance under the symmetries of the network, and improved sharpness on feedforward networks compared to the product of operators' norms, another complexity measure most commonly used. The versatility of the toolkit and its ease of implementation allow us to challenge the concrete promises of path-norm-based generalization bounds, by numerically evaluating the sharpest known bounds for ResNets on ImageNet.  ( 2 min )

If machine learning models were to achieve superhuman abilities at various reasoning or decision-making tasks, how would we go about evaluating such models, given that humans would necessarily be poor proxies for ground truth? In this paper, we propose a framework for evaluating superhuman models via consistency checks. Our premise is that while the correctness of superhuman decisions may be impossible to evaluate, we can still surface mistakes if the model's decisions fail to satisfy certain logical, human-interpretable rules. We instantiate our framework on three tasks where correctness of decisions is hard to evaluate due to either superhuman model abilities, or to otherwise missing ground truth: evaluating chess positions, forecasting future events, and making legal judgments. We show that regardless of a model's (possibly superhuman) performance on these tasks, we can discover logical inconsistencies in decision making. For example: a chess engine assigning opposing valuations to semantically identical boards; GPT-4 forecasting that sports records will evolve non-monotonically over time; or an AI judge assigning bail to a defendant only after we add a felony to their criminal record.  ( 2 min )

In order to trust the predictions of a machine learning algorithm, it is necessary to understand the factors that contribute to those predictions. In the case of probabilistic and uncertainty-aware models, it is necessary to understand not only the reasons for the predictions themselves, but also the model's level of confidence in those predictions. In this paper, we show how existing methods in explainability can be extended to uncertainty-aware models and how such extensions can be used to understand the sources of uncertainty in a model's predictive distribution. In particular, by adapting permutation feature importance, partial dependence plots, and individual conditional expectation plots, we demonstrate that novel insights into model behaviour may be obtained and that these methods can be used to measure the impact of features on both the entropy of the predictive distribution and the log-likelihood of the ground truth labels under that distribution. With experiments using both synthetic and real-world data, we demonstrate the utility of these approaches in understanding both the sources of uncertainty and their impact on model performance.  ( 2 min )

A metric tensor for Riemann manifold Monte Carlo particularly suited for non-linear Bayesian hierarchical models is proposed. The metric tensor is built from symmetric positive semidefinite log-density gradient covariance (LGC) matrices, which are also proposed and further explored here. The LGCs generalize the Fisher information matrix by measuring the joint information content and dependence structure of both a random variable and the parameters of said variable. Consequently, positive definite Fisher/LGC-based metric tensors may be constructed not only from the observation likelihoods as is current practice, but also from arbitrarily complicated non-linear prior/latent variable structures, provided the LGC may be derived for each conditional distribution used to construct said structures. The proposed methodology is highly automatic and allows for exploitation of any sparsity associated with the model in question. When implemented in conjunction with a Riemann manifold variant of the recently proposed numerical generalized randomized Hamiltonian Monte Carlo processes, the proposed methodology is highly competitive, in particular for the more challenging target distributions associated with Bayesian hierarchical models.  ( 2 min )

Physics-informed neural networks have emerged as a coherent framework for building predictive models that combine statistical patterns with domain knowledge. The underlying notion is to enrich the optimization loss function with known relationships to constrain the space of possible solutions. Hydrodynamic simulations are a core constituent of modern cosmology, while the required computations are both expensive and time-consuming. At the same time, the comparatively fast simulation of dark matter requires fewer resources, which has led to the emergence of machine learning algorithms for baryon inpainting as an active area of research; here, recreating the scatter found in hydrodynamic simulations is an ongoing challenge. This paper presents the first application of physics-informed neural networks to baryon inpainting by combining advances in neural network architectures with physical constraints, injecting theory on baryon conversion efficiency into the model loss function. We also introduce a punitive prediction comparison based on the Kullback-Leibler divergence, which enforces scatter reproduction. By simultaneously extracting the complete set of baryonic properties for the Simba suite of cosmological simulations, our results demonstrate improved accuracy of baryonic predictions based on dark matter halo properties, successful recovery of the fundamental metallicity relation, and retrieve scatter that traces the target simulation's distribution.  ( 3 min )

Embodied agents operate in a structured world, often solving tasks with spatial, temporal, and permutation symmetries. Most algorithms for planning and model-based reinforcement learning (MBRL) do not take this rich geometric structure into account, leading to sample inefficiency and poor generalization. We introduce the Equivariant Diffuser for Generating Interactions (EDGI), an algorithm for MBRL and planning that is equivariant with respect to the product of the spatial symmetry group SE(3), the discrete-time translation group Z, and the object permutation group Sn. EDGI follows the Diffuser framework (Janner et al., 2022) in treating both learning a world model and planning in it as a conditional generative modeling problem, training a diffusion model on an offline trajectory dataset. We introduce a new SE(3)xZxSn-equivariant diffusion model that supports multiple representations. We integrate this model in a planning loop, where conditioning and classifier guidance let us softly break the symmetry for specific tasks as needed. On object manipulation and navigation tasks, EDGI is substantially more sample efficient and generalizes better across the symmetry group than non-equivariant models.  ( 2 min )

The recently proposed generative flow networks (GFlowNets) are a method of training a policy to sample compositional discrete objects with probabilities proportional to a given reward via a sequence of actions. GFlowNets exploit the sequential nature of the problem, drawing parallels with reinforcement learning (RL). Our work extends the connection between RL and GFlowNets to a general case. We demonstrate how the task of learning a generative flow network can be efficiently redefined as an entropy-regularized RL problem with a specific reward and regularizer structure. Furthermore, we illustrate the practical efficiency of this reformulation by applying standard soft RL algorithms to GFlowNet training across several probabilistic modeling tasks. Contrary to previously reported results, we show that entropic RL approaches can be competitive against established GFlowNet training methods. This perspective opens a direct path for integrating reinforcement learning principles into the realm of generative flow networks.  ( 2 min )

Inference on modern Bayesian Neural Networks (BNNs) often relies on a variational inference treatment, imposing violated assumptions of independence and the form of the posterior. Traditional MCMC approaches avoid these assumptions at the cost of increased computation due to its incompatibility to subsampling of the likelihood. New Piecewise Deterministic Markov Process (PDMP) samplers permit subsampling, though introduce a model specific inhomogenous Poisson Process (IPPs) which is difficult to sample from. This work introduces a new generic and adaptive thinning scheme for sampling from these IPPs, and demonstrates how this approach can accelerate the application of PDMPs for inference in BNNs. Experimentation illustrates how inference with these methods is computationally feasible, can improve predictive accuracy, MCMC mixing performance, and provide informative uncertainty measurements when compared against other approximate inference schemes.  ( 2 min )

Deep discriminative approaches like random forests and deep neural networks have recently found applications in many important real-world scenarios. However, deploying these learning algorithms in safety-critical applications raises concerns, particularly when it comes to ensuring confidence calibration for both in-distribution and out-of-distribution data points. Many popular methods for in-distribution (ID) calibration, such as isotonic regression and Platt's sigmoidal regression, exhibit excellent ID calibration performance but often at the cost of classification accuracy. Moreover, these methods are not calibrated for the entire feature space, leading to overconfidence in the case of out-of-distribution (OOD) samples. In this paper, we leveraged the fact that deep models, including both random forests and deep-nets, learn internal representations which are unions of polytopes with affine activation functions to conceptualize them both as partitioning rules of the feature space. We replace the affine function in each polytope populated by the training data with a Gaussian kernel. We propose sufficient conditions for our proposed methods to be consistent estimators of the corresponding class conditional densities. Moreover, our experiments on both tabular and vision benchmarks show that the proposed approaches obtain well-calibrated posteriors while mostly preserving or improving the classification accuracy of the original algorithm for in-distribution region, and extrapolates beyond the training data to handle out-of-distribution inputs appropriately.  ( 3 min )

We introduce Cosmos, a framework for object-centric world modeling that is designed for compositional generalization (CG), i.e., high performance on unseen input scenes obtained through the composition of known visual "atoms." The central insight behind Cosmos is the use of a novel form of neurosymbolic grounding. Specifically, the framework introduces two new tools: (i) neurosymbolic scene encodings, which represent each entity in a scene using a real vector computed using a neural encoder, as well as a vector of composable symbols describing attributes of the entity, and (ii) a neurosymbolic attention mechanism that binds these entities to learned rules of interaction. Cosmos is end-to-end differentiable; also, unlike traditional neurosymbolic methods that require representations to be manually mapped to symbols, it computes an entity's symbolic attributes using vision-language foundation models. Through an evaluation that considers two different forms of CG on an established blocks-pushing domain, we show that the framework establishes a new state-of-the-art for CG in world modeling.  ( 2 min )

Whether class labels in a given data set correspond to meaningful clusters is crucial for the evaluation of clustering algorithms using real-world data sets. This property can be quantified by separability measures. A review of the existing literature shows that neither classification-based complexity measures nor cluster validity indices (CVIs) adequately incorporate the central aspects of separability for density-based clustering: between-class separation and within-class connectedness. A newly developed measure (density cluster separability index, DCSI) aims to quantify these two characteristics and can also be used as a CVI. Extensive experiments on synthetic data indicate that DCSI correlates strongly with the performance of DBSCAN measured via the adjusted rand index (ARI) but lacks robustness when it comes to multi-class data sets with overlapping classes that are ill-suited for density-based hard clustering. Detailed evaluation on frequently used real-world data sets shows that DCSI can correctly identify touching or overlapping classes that do not form meaningful clusters.  ( 2 min )

Compressing neural networks is a key step when deploying models for real-time or embedded applications. Factorizing the model's matrices using low-rank approximations is a promising method for achieving compression. While it is possible to set the rank before training, this approach is neither flexible nor optimal. In this work, we propose a post-training rank-selection method called Rank-Tuning that selects a different rank for each matrix. Used in combination with training adaptations, our method achieves high compression rates with no or little performance degradation. Our numerical experiments on signal processing tasks show that we can compress recurrent neural networks up to 14x with at most 1.4% relative performance reduction.  ( 2 min )

Many modern statistical analysis and machine learning applications require training models on sensitive user data. Differential privacy provides a formal guarantee that individual-level information about users does not leak. In this framework, randomized algorithms inject calibrated noise into the confidential data, resulting in privacy-protected datasets or queries. However, restricting access to only the privatized data during statistical analysis makes it computationally challenging to perform valid inferences on parameters underlying the confidential data. In this work, we propose simulation-based inference methods from privacy-protected datasets. Specifically, we use neural conditional density estimators as a flexible family of distributions to approximate the posterior distribution of model parameters given the observed private query results. We illustrate our methods on discrete time-series data under an infectious disease model and on ordinary linear regression models. Illustrating the privacy-utility trade-off, our experiments and analysis demonstrate the necessity and feasibility of designing valid statistical inference procedures to correct for biases introduced by the privacy-protection mechanisms.  ( 2 min )

The key challenge underlying machine learning is generalisation to new data. This work studies generalisation for datasets consisting of related tasks that may differ in causal mechanisms. For example, observational medical data for complex diseases suffers from heterogeneity in causal mechanisms of disease across patients, creating challenges for machine learning algorithms that need to generalise to new patients outside of the training dataset. Common approaches for learning supervised models with heterogeneous datasets include learning a global model for the entire dataset, learning local models for each tasks' data, or utilising hierarchical, meta-learning and multi-task learning approaches to learn how to generalise from data pooled across multiple tasks. In this paper we propose causal similarity-based hierarchical Bayesian models to improve generalisation to new tasks by learning how to pool data from training tasks with similar causal mechanisms. We apply this general modelling principle to Bayesian neural networks and compare a variety of methods for estimating causal task similarity (for both known and unknown causal models). We demonstrate the benefits of our approach and applicability to real world problems through a range of experiments on simulated and real data.  ( 2 min )

We propose in this paper, STANLEY, a STochastic gradient ANisotropic LangEvin dYnamics, for sampling high dimensional data. With the growing efficacy and potential of Energy-Based modeling, also known as non-normalized probabilistic modeling, for modeling a generative process of different natures of high dimensional data observations, we present an end-to-end learning algorithm for Energy-Based models (EBM) with the purpose of improving the quality of the resulting sampled data points. While the unknown normalizing constant of EBMs makes the training procedure intractable, resorting to Markov Chain Monte Carlo (MCMC) is in general a viable option. Realizing what MCMC entails for the EBM training, we propose in this paper, a novel high dimensional sampling method, based on an anisotropic stepsize and a gradient-informed covariance matrix, embedded into a discretized Langevin diffusion. We motivate the necessity for an anisotropic update of the negative samples in the Markov Chain by the nonlinearity of the backbone of the EBM, here a Convolutional Neural Network. Our resulting method, namely STANLEY, is an optimization algorithm for training Energy-Based models via our newly introduced MCMC method. We provide a theoretical understanding of our sampling scheme by proving that the sampler leads to a geometrically uniformly ergodic Markov Chain. Several image generation experiments are provided in our paper to show the effectiveness of our method.  ( 2 min )

Due to the increasing use of Machine Learning models in high stakes decision making settings, it has become increasingly important to have tools to understand how models arrive at decisions. Assuming a trained Supervised Classification model, explanations can be obtained via counterfactual analysis: a counterfactual explanation of an instance indicates how this instance should be minimally modified so that the perturbed instance is classified in the desired class by the Machine Learning classification model. Most of the Counterfactual Analysis literature focuses on the single-instance single-counterfactual setting, in which the analysis is done for one single instance to provide one single explanation. Taking a stakeholder's perspective, in this paper we introduce the so-called collective counterfactual explanations. By means of novel Mathematical Optimization models, we provide a counterfactual explanation for each instance in a group of interest, so that the total cost of the perturbations is minimized under some linking constraints. Making the process of constructing counterfactuals collective instead of individual enables us to detect the features that are critical to the entire dataset to have the individuals classified in the desired class. Our methodology allows for some instances to be treated individually, performing the collective counterfactual analysis for a fraction of records of the group of interest. This way, outliers are identified and handled appropriately. Under some assumptions on the classifier and the space in which counterfactuals are sought, finding collective counterfactuals is reduced to solving a convex quadratic linearly constrained mixed integer optimization problem, which, for datasets of moderate size, can be solved to optimality using existing solvers. The performance of our approach is illustrated on real-world datasets, demonstrating its usefulness.  ( 3 min )

The training and generalization dynamics of the Transformer's core mechanism, namely the Attention mechanism, remain under-explored. Besides, existing analyses primarily focus on single-head attention. Inspired by the demonstrated benefits of overparameterization when training fully-connected networks, we investigate the potential optimization and generalization advantages of using multiple attention heads. Towards this goal, we derive convergence and generalization guarantees for gradient-descent training of a single-layer multi-head self-attention model, under a suitable realizability condition on the data. We then establish primitive conditions on the initialization that ensure realizability holds. Finally, we demonstrate that these conditions are satisfied for a simple tokenized-mixture model. We expect the analysis can be extended to various data-model and architecture variations.  ( 2 min )

In theoretical ML, the teacher-student paradigm is often employed as an effective metaphor for real-life tuition. The above scheme proves particularly relevant when the student network is overparameterized as compared to the teacher network. Under these operating conditions, it is tempting to speculate that the student ability to handle the given task could be eventually stored in a sub-portion of the whole network. This latter should be to some extent reminiscent of the frozen teacher structure, according to suitable metrics, while being approximately invariant across different architectures of the student candidate network. Unfortunately, state-of-the-art conventional learning techniques could not help in identifying the existence of such an invariant subnetwork, due to the inherent degree of non-convexity that characterizes the examined problem. In this work, we take a leap forward by proposing a radically different optimization scheme which builds on a spectral representation of the linear transfer of information between layers. The gradient is hence calculated with respect to both eigenvalues and eigenvectors with negligible increase in terms of computational and complexity load, as compared to standard training algorithms. Working in this framework, we could isolate a stable student substructure, that mirrors the true complexity of the teacher in terms of computing neurons, path distribution and topological attributes. When pruning unimportant nodes of the trained student, as follows a ranking that reflects the optimized eigenvalues, no degradation in the recorded performance is seen above a threshold that corresponds to the effective teacher size. The observed behavior can be pictured as a genuine second-order phase transition that bears universality traits.  ( 3 min )

We commonly encounter the problem of identifying an optimally weight adjusted version of the empirical distribution of observed data, adhering to predefined constraints on the weights. Such constraints often manifest as restrictions on the moments, tail behaviour, shapes, number of modes, etc., of the resulting weight adjusted empirical distribution. In this article, we substantially enhance the flexibility of such methodology by introducing a nonparametrically imbued distributional constraints on the weights, and developing a general framework leveraging the maximum entropy principle and tools from optimal transport. The key idea is to ensure that the maximum entropy weight adjusted empirical distribution of the observed data is close to a pre-specified probability distribution in terms of the optimal transport metric while allowing for subtle departures. The versatility of the framework is demonstrated in the context of three disparate applications where data re-weighting is warranted to satisfy side constraints on the optimization problem at the heart of the statistical task: namely, portfolio allocation, semi-parametric inference for complex surveys, and ensuring algorithmic fairness in machine learning algorithms.  ( 2 min )

Manifold learning flows are a class of generative modelling techniques that assume a low-dimensional manifold description of the data. The embedding of such manifold into the high-dimensional space of the data is achieved via learnable invertible transformations. Therefore, once the manifold is properly aligned via a reconstruction loss, the probability density is tractable on the manifold and maximum likelihood can be used optimize the network parameters. Naturally, the lower-dimensional representation of the data requires an injective-mapping. Recent approaches were able to enforce that density aligns with the modelled manifold, while efficiently calculating the density volume-change term when embedding to the higher-dimensional space. However, unless the injective-mapping is analytically predefined, the learned manifold is not necessarily an efficient representation of the data. Namely, the latent dimensions of such models frequently learn an entangled intrinsic basis with degenerate information being stored in each dimension. Alternatively, if a locally orthogonal and/or sparse basis is to be learned, here coined canonical intrinsic basis, it can serve in learning a more compact latent space representation. Towards this end, we propose a canonical manifold learning flow method, where a novel optimization objective enforces the transformation matrix to have few prominent and orthogonal basis functions. Canonical manifold flow yields a more efficient use of the latent space, automatically generating fewer prominent and distinct dimensions to represent data, and consequently a better approximation of target distributions than other manifold flow methods in most experiments we conducted, resulting in lower FID scores.  ( 2 min )

Entropy maximization and free energy minimization are general physical principles for modeling the dynamics of various physical systems. Notable examples include modeling decision-making within the brain using the free-energy principle, optimizing the accuracy-complexity trade-off when accessing hidden variables with the information bottleneck principle (Tishby et al., 2000), and navigation in random environments using information maximization (Vergassola et al., 2007). Built on this principle, we propose a new class of bandit algorithms that maximize an approximation to the information of a key variable within the system. To this end, we develop an approximated analytical physics-based representation of an entropy to forecast the information gain of each action and greedily choose the one with the largest information gain. This method yields strong performances in classical bandit settings. Motivated by its empirical success, we prove its asymptotic optimality for the two-armed bandit problem with Gaussian rewards. Owing to its ability to encompass the system's properties in a global physical functional, this approach can be efficiently adapted to more complex bandit settings, calling for further investigation of information maximization approaches for multi-armed bandit problems.  ( 2 min )

We study the performance of empirical risk minimization on the $p$-norm linear regression problem for $p \in (1, \infty)$. We show that, in the realizable case, under no moment assumptions, and up to a distribution-dependent constant, $O(d)$ samples are enough to exactly recover the target. Otherwise, for $p \in [2, \infty)$, and under weak moment assumptions on the target and the covariates, we prove a high probability excess risk bound on the empirical risk minimizer whose leading term matches, up to a constant that depends only on $p$, the asymptotically exact rate. We extend this result to the case $p \in (1, 2)$ under mild assumptions that guarantee the existence of the Hessian of the risk at its minimizer.  ( 2 min )

The quality of explanations for the predictions of complex machine learning predictors is often measured using insertion and deletion metrics, which assess the faithfulness of the explanations, i.e., how correctly the explanations reflect the predictor's behavior. To improve the faithfulness, we propose insertion/deletion metric-aware explanation-based optimization (ID-ExpO), which optimizes differentiable predictors to improve both insertion and deletion scores of the explanations while keeping their predictive accuracy. Since the original insertion and deletion metrics are indifferentiable with respect to the explanations and directly unavailable for gradient-based optimization, we extend the metrics to be differentiable and use them to formalize insertion and deletion metric-based regularizers. The experimental results on image and tabular datasets show that the deep neural networks-based predictors fine-tuned using ID-ExpO enable popular post-hoc explainers to produce more faithful and easy-to-interpret explanations while keeping high predictive accuracy.  ( 2 min )

In the realm of deep learning, transformers have emerged as a dominant architecture, particularly in natural language processing tasks. However, with their widespread adoption, concerns regarding the security and privacy of the data processed by these models have arisen. In this paper, we address a pivotal question: Can the data fed into transformers be recovered using their attention weights and outputs? We introduce a theoretical framework to tackle this problem. Specifically, we present an algorithm that aims to recover the input data $X \in \mathbb{R}^{d \times n}$ from given attention weights $W = QK^\top \in \mathbb{R}^{d \times d}$ and output $B \in \mathbb{R}^{n \times n}$ by minimizing the loss function $L(X)$. This loss function captures the discrepancy between the expected output and the actual output of the transformer. Our findings have significant implications for the Localized Layer-wise Mechanism (LLM), suggesting potential vulnerabilities in the model's design from a security and privacy perspective. This work underscores the importance of understanding and safeguarding the internal workings of transformers to ensure the confidentiality of processed data.  ( 2 min )

Stochastic processes defined on integer valued state spaces are popular within the physical and biological sciences. These models are necessary for capturing the dynamics of small systems where the individual nature of the populations cannot be ignored and stochastic effects are important. The inference of the parameters of such models, from time series data, is difficult due to intractability of the likelihood; current methods, based on simulations of the underlying model, can be so computationally expensive as to be prohibitive. In this paper we construct a neural likelihood approximation for integer valued time series data using causal convolutions, which allows us to evaluate the likelihood of the whole time series in parallel. We demonstrate our method by performing inference on a number of ecological and epidemiological models, showing that we can accurately approximate the true posterior while achieving significant computational speed ups in situations where current methods struggle.  ( 2 min )

Score-based generative models (SGMs) sample from a target distribution by iteratively transforming noise using the score function of the perturbed target. For any finite training set, this score function can be evaluated in closed form, but the resulting SGM memorizes its training data and does not generate novel samples. In practice, one approximates the score by training a neural network via score-matching. The error in this approximation promotes generalization, but neural SGMs are costly to train and sample, and the effective regularization this error provides is not well-understood theoretically. In this work, we instead explicitly smooth the closed-form score to obtain an SGM that generates novel samples without training. We analyze our model and propose an efficient nearest-neighbor-based estimator of its score function. Using this estimator, our method achieves sampling times competitive with neural SGMs while running on consumer-grade CPUs.  ( 2 min )

We initiate a novel approach to explain the out of sample performance of random forest (RF) models by exploiting the fact that any RF can be formulated as an adaptive weighted K nearest-neighbors model. Specifically, we use the proximity between points in the feature space learned by the RF to re-write random forest predictions exactly as a weighted average of the target labels of training data points. This linearity facilitates a local notion of explainability of RF predictions that generates attributions for any model prediction across observations in the training set, and thereby complements established methods like SHAP, which instead generates attributions for a model prediction across dimensions of the feature space. We demonstrate this approach in the context of a bond pricing model trained on US corporate bond trades, and compare our approach to various existing approaches to model explainability.  ( 2 min )

High-dimensional longitudinal data is increasingly used in a wide range of scientific studies. However, there are few statistical methods for high-dimensional linear mixed models (LMMs), as most Bayesian variable selection or penalization methods are designed for independent observations. Additionally, the few available software packages for high-dimensional LMMs suffer from scalability issues. This work presents an efficient and accurate Bayesian framework for high-dimensional LMMs. We use empirical Bayes estimators of hyperparameters for increased flexibility and an Expectation-Conditional-Minimization (ECM) algorithm for computationally efficient maximum a posteriori probability (MAP) estimation of parameters. The novelty of the approach lies in its partitioning and parameter expansion as well as its fast and scalable computation. We illustrate Linear Mixed Modeling with PaRtitiOned empirical Bayes ECM (LMM-PROBE) in simulation studies evaluating fixed and random effects estimation along with computation time. A real-world example is provided using data from a study of lupus in children, where we identify genes and clinical factors associated with a new lupus biomarker and predict the biomarker over time.  ( 2 min )

Molecular language modeling is an effective approach to generating novel chemical structures. However, these models do not \emph{a priori} encode certain preferences a chemist may desire. We investigate the use of fine-tuning using Direct Preference Optimization to better align generated molecules with chemist preferences. Our findings suggest that this approach is simple, efficient, and highly effective.  ( 2 min )

Oracle has partnered with Nvidia to bring Nvidia's AI stack to its marketplace, giving Oracle customers access to top-of-the-line GPUs for training models and building generative applications. Eligible enterprises can purchase Nvidia's DGX Cloud AI supercomputing platform and AI Enterprise software directly from the marketplace and start training models for deployment on the Oracle Cloud Infrastructure. Nvidia DGX Cloud offers a serverless experience for multi-node training of custom generative AI models, supporting near-limitless scale of GPU resources. Nvidia AI Enterprise helps teams accelerate the deployment of models to production, with features such as the Nvidia NeMo framework, Rapids, TensorRT LLM open-source library, and Triton Inference server. Oracle has been focused on industry partnerships for its AI efforts and has announced generative AI capabilities in its products and solutions. Source : https://venturebeat.com/ai/oracle-loops-in-nvidias-ai-stack-for-end-to-end-model-development/ submitted by /u/NuseAI [link] [comments]

Hello, I would love to have an LLm that can provide answers (in chat format) based some of the sql db data we have. Want it for an internal company project. I am by no means an expert but decent in programming and want to build a system to get answers in chat format. My understanding is that training LLMs ground up is prohibitively expensive and langchains are sort of hybrid , efficient solutions. Please suggest any other solutions. Also would Langchain being a company and not open source pose a problem in terms of copyrights? Thanks! submitted by /u/betelgeuseian [link] [comments]  ( 9 min )

Paper: https://arxiv.org/abs/2310.08560 Github: https://github.com/cpacker/MemGPT Blog: https://memgpt.ai/ Youtube: https://youtu.be/QQ2QOPWZKVc?si=_bSSXU9EQE0FP64h MemGPT 🧠 Giving AI Unlimited Prompt Size (Big Step Towards AGI?) by Metthew Berman / Must watch and he also explains how to install it! Overview LLMs are increasingly being used for perpetual chats Limited context lengths makes perpetual chat challenging MemGPT manages a virtual context (inspired by virtual memory in operating systems) to create unbounded LLM context With MemGPT, we demonstrate that LLMs can be taught to manage their own memory! Abstract: Large language models (LLMs) have revolutionized AI, but are constrained by limited context windows, hindering their utility in tasks like extended conversa…  ( 9 min )

Hi, I am a mac user. I am trying to use whisper.cpp downloaded from its github file. I don't know much about phyton or coding so I basically followed this guide to install and use it. I downloaded the large model to try it. I am using it for non-English languages and I want to use it for language learning purposes so I can understand what is being said in an Instagram story or a Youtube video (without subtitles) or a tv series or an extract of movie etc. I was using Macwhisper but I wanted to try the pro features and I don't want to pay for it (for now) and try the pro models for non-English languages. My question is: all of my files that I want to transcribe are video files with .mp4 extension. Can I also transcribe those with whisper? If not, and if I can only transcribe audio files, can it be .mp3? I understand that I need to install and use ffmpeg. Does it support mp3? Also, as I understand, the transcripted text will appear in the terminal. Can I export it in -srt or pdf? Thanks submitted by /u/toughytough [link] [comments]  ( 9 min )

I've watched an interesting video: Deriving the Ultimate Neural Network Architecture from Scratch. It's about how to come up to the transformer architecture when you have an understanding of CNNs. The crux of it is an idea of pairwise convolutional layers. The first layer applies not to the sequence of words itself, but to all pairs of words in the sentence. This ensures that each relation of words that are far from each other is taken into account. The next convolutional layer applies to all pairs of results of the previous one. This way longer subsequences of words are factored in. pairs of words My question is: are there any articles on how transformers were invented? I see a lot of explanations of the original paper, but at best they all answer the question how transformers work. But why is the architecture the way it is? Was it discovered like the video describes? Or the path was more convoluted? I'd like to know more about this connection. Anyway, it would be great to figure out in all details how these pairwise layers are related to the concepts of query, key, and value. Here's what the author of the video wrote in comments: Yeah it's a term I made up so you won't find it in any sources, sorry about that. Usually sources will just talk about self attention in terms of key, query and value lookups, so you can look at those to get a more detailed understanding of the transformer. The value transform is equivalent to the linear representation function I use in the pairwise convolution, the key and query attention scores are equivalent to the bi-linear form scoring function I use (with the bi-linear form weight matrix given by Q^TK). I chose to use this unusual terminology because, personally, I feel the key, query and value terminology comes out of nowhere, and I wanted to connect the transformer more directly to its predecessor (the CNN). ​ submitted by /u/Veson [link] [comments]  ( 10 min )

I'm trying to fit a multivariate LSTM model to time series data to predict future values for one relatively noisy series. I noticed that the the loss (mse in this case) is pretty high given that the data has been standardized beforehand. So I really have two questions: why is the mse so high and is the learning curve indicative of any obvious problems? Thank you! https://preview.redd.it/r9bel6p7kfvb1.png?width=547&format=png&auto=webp&s=4eee53aa8005da8a89f330f6e98fe6cadde3467e submitted by /u/DifferenceUnhappy393 [link] [comments]  ( 9 min )

Sutton and Barto’s textbook mentions that combing off-policy learning, bootstrapping, and function approximation leads to extreme instability and should be avoided. Yet when I encounter a reinforcement problem in the wild and look how people go about solving it, if someone’s solution involves bootstrapping more often than not it’s some variation of deep Q-learning. Why is this? submitted by /u/BiasedEstimators [link] [comments]

Hey everyone, I'm learning ML but i'm barely scratching the terminologies. 2 years ago I couldn't code anything but with school (python,sql and R) I learned fundamentals. I also have access to code academy. My current program is very machine learning/deep learning focused. On the side I DM a d&d game. Within the context of the world (eberron) robots are common. With my ADHD and being a new DM I want to outsource lore questions might have (that I would have to look up and slow down the game). The concept is to have a GUI and have the player interact with the chat bot. I've gotten to a proof of concept workflow. On Google colab. Thanks to langchain I managed to ingest pdfs and a url. Make then a directory, Embedded the text, bring it into a vector dB. Have the llm pull from the vector. Answer the question. Now I don't know what to do. I tried to bring the colab notebook onto Google cloud. But now cloud is becoming a rabbit home with vertex and docAI...and I don't want to deep dive into that, if it's a outside the scope of this "project" I'd appreciate any advice, links...etc. I got a limited success in botpress using a single pdf. It works but feel unsatisfying. N8N looks promising but if it's not intuitive then I don't want to go down that road. If I posted in the wrong group please direct me to the correct one. submitted by /u/work929 [link] [comments]  ( 9 min )

https://arxiv.org/abs/2310.10638 "Large language models (LMs) are currently trained to predict tokens given document prefixes, enabling them to directly perform long-form generation and prompting-style tasks which can be reduced to document completion. Existing pretraining pipelines train LMs by concatenating random sets of short documents to create input contexts but the prior documents provide no signal for predicting the next document. We instead present In-Context Pretraining, a new approach where language models are pretrained on a sequence of related documents, thereby explicitly encouraging them to read and reason across document boundaries. We can do In-Context Pretraining by simply changing the document ordering so that each context contains related documents, and directly applying existing pretraining pipelines. However, this document sorting problem is challenging. There are billions of documents and we would like the sort to maximize contextual similarity for every document without repeating any data. To do this, we introduce approximate algorithms for finding related documents with efficient nearest neighbor search and constructing coherent input contexts with a graph traversal algorithm. Our experiments show In-Context Pretraining offers a simple and scalable approach to significantly enhance LMs'performance: we see notable improvements in tasks that require more complex contextual reasoning, including in-context learning (+8%), reading comprehension (+15%), faithfulness to previous contexts (+16%), long-context reasoning (+5%), and retrieval augmentation (+9%)." submitted by /u/Parking-Priority6217 [link] [comments]  ( 9 min )

Greetings, I'm on my 2nd year of College (Artificial Intelligence bachelors degree), and currently making a group project that will require machine learning. The project consists of managing and regulating the conditions (temperature, humidity, lightning, etc.) of the environment that surrounds important products (vaccines, human organs, etc.) during their transportation, using sensors implemented in their transportation box. For that being possible, our group was planning to use a predictive model using machine learning, to prevent cases such as the exposure of inappropriate temperature levels, that could damage the product, and subsequently taking the appropriate measures to improve the environment, before it reaches such dangerous scenarios. Therefore, I would like to know which tools and skills will be needed and helpful in order to achieve such goal. If you have any advice, that'll be very much appreciated. :) submitted by /u/Storm2003 [link] [comments]  ( 9 min )

Researchers propose a new AI system called 3D-GPT that creates 3D models by combining natural language instructions and agents specialized for working with existing 3D modeling tools. 3D-GPT has predefined functions that make 3D shapes, and it tweaks parameters to build scenes. The key is getting the AI to understand instructions and pick the right tools. It has three main agents: A dispatcher that parses the text and picks generation functions A conceptualizer that adds details missing from the description A modeler that sets parameters and outputs code to drive 3D software By breaking modeling work down into steps, the agents can collab to match the descriptions. This is sort of like how a 3D modeling team of humans would work. The paper authors show it making simple scenes like "lush meadow with flowers" that fit the text. It also modifies scenes appropriately when given new instructions. I include some gifs of example outputs in my full summary. They look pretty good - I would say 2005-quality graphics. There are limits. It fully relies on existing generators, so quality is capped. Details and curves are iffy. It resorts to default shapes often instead of true understanding. And I doubt the verts and textures are well-optimized. The agent architecture seems to be really popular right now. This one shows some planning skills, which could extend to more creative tasks someday. TLDR: AI agents can team up to generate 3D models from text instructions. Works to some degree but limitations remain. Full summary. Paper here. submitted by /u/Successful-Western27 [link] [comments]  ( 9 min )

Link: https://ojs.aaai.org/index.php/AAAI/article/view/25726/25498 Abstract: We study the combinatorial assignment domain, which includes combinatorial auctions and course allocation. The main challenge in this domain is that the bundle space grows exponentially in the number of items. To address this, several papers have recently proposed machine learning-based preference elicitation algorithms that aim to elicit only the most important information from agents. However, the main shortcoming of this prior work is that it does not model a mechanism's uncertainty over values for not yet elicited bundles. In this paper, we address this shortcoming by presenting a Bayesian optimization-based combinatorial assignment (BOCA) mechanism. Our key technical contribution is to integrate a method for capturing model uncertainty into an iterative combinatorial auction mechanism. Concretely, we design a new method for estimating an upper uncertainty bound that can be used to define an acquisition function to determine the next query to the agents. This enables the mechanism to properly explore (and not just exploit) the bundle space during its preference elicitation phase. We run computational experiments in several spectrum auction domains to evaluate BOCA's performance. Our results show that BOCA achieves higher allocative efficiency than state-of-the-art approaches. https://preview.redd.it/aeo36u3wldvb1.png?width=1288&format=png&auto=webp&s=2982547f8af51ed7195f49dbec9359fecba1693f ​ submitted by /u/Yossarian_1234 [link] [comments]  ( 9 min )

So a lot of multimodal models I've seen use a linear layer to transform encoded image/video/audio into the multimodal LLMs embedding space. This makes sense for the input, but how would output work? Normally you use a layer to convert the embedding to a SoftMax of probabilities of possible output tokens. This makes sense for discrete outputs like tokens but not for continuous outputs like images or audio. ​ submitted by /u/30299578815310 [link] [comments]  ( 9 min )

The title says it all. I agree what they did is incredible and literally changed AI landscape in last couple of years. But I’m getting tired of everyone acting like OpenAI is the only one doing great research. The twit-fluencers praising even the slightest peep from them. I don’t understand this fanaticism in AI community. There are smart researchers doing smart things all over the world. But they don’t even get a fraction of appreciation they deserve. And the strangest thing of all, ChatGPT is used as oracle to evaluate models in research papers. Consistency models are extremely meh and if it did not come out of openAI, people would’ve forgotten them a long time ago! Edit 1: I’m in grad school and that’s all a lot of students around me talk about/ chase. I want to work on a bit more fundamental problems, but I feel like I’m being left behind. Edit 2: This post is mostly a rant about academics obsessed with OpenAI research/products and LLMs. submitted by /u/mildlyphd [link] [comments]  ( 9 min )

Hey everyone,we have published an article about Hacktoberfest Projects 🎃 medium.com with a curated list of open-source machine learning GUI projects built with javascript or typescript. ​ https://preview.redd.it/nr4jfbqoscvb1.png?width=1352&format=png&auto=webp&s=fbb2313aabf0a617b6e426f1fa5018946b7ed7f5 🔍 Finding machine learning projects that are suitable for JS/TS developers during Hacktoberfest can be daunting due to the overwhelming abundance of open-source projects. We’ve simplified this process, offering you a refined selection of opportunities where your coding skills can shine and make a real impact. The Selection includes: Spotlight our powerful tool for intuitively exploring unstructured datasets directly from dataframes. Iteratives CML (Continuous Machine Learning) a command-line interface tool designed to enhance continuous integration and delivery (CI/CD) workflows. Inclusive Code Reviews: Browser Extension for improving online comments such as code reviews on Github or Azure DevOps. BeatBridge - A Music Player with a Recommendation Engine Each project offers a unique blend of challenges and learning opportunities, inviting you to contribute and grow your skills and knowledge in the dynamic world of open source. Choose a project that resonates with you, select an issue, and make an impact 🚀. submitted by /u/DocBrownMS [link] [comments]  ( 9 min )

Paper: https://arxiv.org/abs/2310.12823 Github: https://github.com/THUDM/AgentTuning Model: https://huggingface.co/THUDM/agentlm-70b Abstract: Open large language models (LLMs) with great performance in various tasks have significantly advanced the development of LLMs. However, they are far inferior to commercial models such as ChatGPT and GPT-4 when acting as agents to tackle complex tasks in the real world. These agent tasks employ LLMs as the central controller responsible for planning, memorization, and tool utilization, necessitating both fine-grained prompting methods and robust LLMs to achieve satisfactory performance. Though many prompting methods have been proposed to complete particular agent tasks, there is lack of research focusing on improving the agent capabilities of L…  ( 9 min )

I'm wondering whether LLMs within your organisation are widely used (including non-programmers), and in an (official) capacity that prevents OpenAI/Microsoft or another third party from using the input. Here, I'm talking about access by a wide variety of employees, not including as part of a data pipeline that doesn't have a user interface and only performs one job. Does your organization have a custom-built interface with enterprise access to an LLM? Use one of the open-source interfaces, or does your organisation provide access through i.e. Microsoft copilot? What about access to Github copilot (for programmers)? Or does your organisation have some kind of SAAS solution? If you have some kind of RAG within the organisation that isn't built-in into a product. What sort of stack do you use? Do you use OpenAI plugins to access this? submitted by /u/Background_Claim7907 [link] [comments]  ( 9 min )

Been really interested in Open-Domain Question Answering these days and saw some interesting new models apart from the typical Retriever-Reader e.g. Generator-Retriever-Generator. Anyone particularly excited about anything new in the field - some new technique/model etc.? submitted by /u/Aggravating-Floor-38 [link] [comments]  ( 9 min )

The State of AI Report for this year is out : https://www.stateof.ai/2023-report-launch A 160-slide presentation/report which seems quite exhaustive in the discussed topics, and provides a good view of the "hottest" research axes this year. Previous reports (yearly since 2019) are available on their website and have been generally well received in this sub. submitted by /u/ElkoSoltius [link] [comments]  ( 9 min )

https://arxiv.org/abs/2310.01714 "Chain-of-thought (CoT) prompting for language models demonstrates impressive performance across reasoning tasks, but typically needs labeled exemplars of the reasoning process. In this work, we introduce a new prompting approach, Analogical Prompting, designed to automatically guide the reasoning process of large language models. Inspired by analogical reasoning, a cognitive process in which humans draw from relevant past experiences to tackle new problems, our approach prompts language models to self-generate relevant exemplars or knowledge in the context, before proceeding to solve the given problem. This method presents several advantages: it obviates the need for labeling or retrieving exemplars, offering generality and convenience; it can also tailor the generated exemplars and knowledge to each problem, offering adaptability. Experimental results show that our approach outperforms 0-shot CoT and manual few-shot CoT in a variety of reasoning tasks, including math problem solving in GSM8K and MATH, code generation in Codeforces, and other reasoning tasks in BIG-Bench." https://preview.redd.it/f9azq40pwavb1.jpg?width=6390&format=pjpg&auto=webp&s=0af3de7925a6ef8f442e40f952849db2f544c3a7 submitted by /u/Parking-Priority6217 [link] [comments]

https://arxiv.org/abs/2309.03409 "Optimization is ubiquitous. While derivative-based algorithms have been powerful tools for various problems, the absence of gradient imposes challenges on many real-world applications. In this work, we propose Optimization by PROmpting (OPRO), a simple and effective approach to leverage large language models (LLMs) as optimizers, where the optimization task is described in natural language. In each optimization step, the LLM generates new solutions from the prompt that contains previously generated solutions with their values, then the new solutions are evaluated and added to the prompt for the next optimization step. We first showcase OPRO on linear regression and traveling salesman problems, then move on to prompt optimization where the goal is to find instructions that maximize the task accuracy. With a variety of LLMs, we demonstrate that the best prompts optimized by OPRO outperform human-designed prompts by up to 8% on GSM8K, and by up to 50% on Big-Bench Hard tasks." submitted by /u/Parking-Priority6217 [link] [comments]  ( 9 min )

I’ve seen many comments telling people to go to r/singularity, so I’ve been wondering about the communities thoughts on non-technical subreddits. Are they seen as a source of hype, getting newcomers more interested in the field and helping to advance knowledge? Or do you see such communities as an overly optimistic non-skeptical massive misinformation/active disinformation center? Do you think there’s something that can be done to improve these communities? What do you think their role should be relative to the technical communities? Do you have any specific criticisms? For those of you who think our two communities should be separate to what extent? submitted by /u/Username912773 [link] [comments]  ( 9 min )

paper: https://arxiv.org/abs/2301.12321 code: https://github.com/snu-mllab/Neural-Relation-Graph TDLR: We present a scalable and domain-agnostic approach utilizing the relational structure of data for identifying label noise and outliers https://preview.redd.it/o9k7kliqe9vb1.png?width=3108&format=png&auto=webp&s=b7c34bd7f4bc130915440986570104f9bebd4f07 Diagnosing and cleaning data is a crucial step for building robust machine learning systems. However, identifying problems within large-scale datasets with real-world distributions is challenging due to the presence of complex issues such as label errors, under-representation, and outliers. In this paper, we propose a unified approach for identifying the problematic data by utilizing a largely ignored source of information: a relational structure of data in the feature-embedded space. To this end, we present scalable and effective algorithms for detecting label errors and outlier data based on the relational graph structure of data. We further introduce a visualization tool that provides contextual information of a data point in the feature-embedded space, serving as an effective tool for interactively diagnosing data. We evaluate the label error and outlier/out-of-distribution (OOD) detection performances of our approach on the large-scale image, speech, and language domain tasks, including ImageNet, ESC-50, and SST2. Our approach achieves state-of-the-art detection performance on all tasks considered and demonstrates its effectiveness in debugging large-scale real-world datasets across various domains. ​ Detected samples with label error (red colored) from ImageNet (top) and SST2 (bottom). ​ Detected outlier samples from ImageNet (top) and SST2 (bottom) validation sets. submitted by /u/janghyun1230 [link] [comments]  ( 9 min )

Is there a future where models can run efficiently and at scale with just half a dozen high end consumer GPUs? A lot of people seem to think the bottleneck is "there's no competition for NVIDIA" but I actually think the current bottleneck is software. 4x 4090s is more CUDA cores, more transistors, more VRAM than a H100, but the performance and price difference is staggering, which should not be the case. Raspberry Pi 4s running faster desktops than a same generation Dell Inspiron prove that software integration is key. Cheap performance is laying on the table, it just has to be used more effectively by models and ML libraries submitted by /u/HovercraftForeign591 [link] [comments]  ( 9 min )

Hi everyone, greeting from south america.. Basically I'm looking for an program to learn and improve my job opportunities in the MLOps field and at some point getting higher responsability positions. I recently got admitted for both OMSA and OMSCS from Gatech, but I feel those programs are more focused on the data science side of things. Is there any other alternative without GRE requeriment that you would recommend with a similar cost? Maybe I'm wrong about the aforementioned programs, if you think so, please let me know why. Thanks! submitted by /u/imatiasmb [link] [comments]  ( 9 min )

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Reddit is considering implementing a soft paywall on its content if generative AI companies do not agree to pay for using its data. This move comes as tensions rise between tech giants and content publishers over the financial stakes in the generative AI market. Reddit believes that its vast range of user-generated text makes it a goldmine for AI training data, but critics argue that much of the content is copied from other sources or links to third-party resources. Enforcing a soft paywall could provide leverage in negotiations with AI companies, but it may also alienate the Reddit community and impede the discovery of new content. Major newspapers like The New York Times and The Washington Post have also blocked AI companies from scraping their websites for training data. Enforcing a soft paywall is a double-edged sword for Reddit, as it could provide leverage in negotiations but also alienate the community and impede content discovery. Reddit's broken search engine is a major concern, and implementing a paywall could result in a significant loss of search traffic. If Reddit and other content giants implement paywalls, it could impact how generative AI models are trained and lead to increased expenses and a slower rate of innovation. This move by Reddit may pave the way for more publishers and platforms to implement paywalls, potentially reshuffling the industry. Source : https://stackdiary.com/reddit-thinks-its-data-is-worth-enforcing-a-log-in-page/ submitted by /u/NuseAI [link] [comments]

Researchers propose a new AI system called 3D-GPT that creates 3D models by combining natural language instructions and agents specialized for working with existing 3D modeling tools. 3D-GPT has predefined functions that make 3D shapes, and it tweaks parameters to build scenes. The key is getting the AI to understand instructions and pick the right tools. It has three main agents: A dispatcher that parses the text and picks generation functions A conceptualizer that adds details missing from the description A modeler that sets parameters and outputs code to drive 3D software By breaking modeling work down into steps, the agents can collab to match the descriptions. This is sort of like how a 3D modeling team of humans would work. The paper authors show it making simple scenes like "lush meadow with flowers" that fit the text. It also modifies scenes appropriately when given new instructions. I include some gifs of example outputs in my full summary. They look pretty good - I would say 2005-quality graphics. There are limits. It fully relies on existing generators, so quality is capped. Details and curves are iffy. It resorts to default shapes often instead of true understanding. And I doubt the verts and textures are well-optimized. The agent architecture seems to be really popular right now. This one shows some planning skills, which could extend to more creative tasks someday. TLDR: AI agents can team up to generate 3D models from text instructions. Works to some degree but limitations remain. Full summary. Paper here. submitted by /u/Successful-Western27 [link] [comments]

News provided by aibrews.com ​ Adept open-sources Fuyu-8B - a multimodal model designed from the ground up for digital agents, so it can support arbitrary image resolutions, answer questions about graphs and diagrams, answer UI-based questions and more. It has a much simpler architecture and training procedure than other multi-modal models- there is no image encoder [Details]. Meta AI researchers present an AI system that can be deployed in real time to reconstruct, from brain activity, the images perceived and processed by the brain at each instant. It uses magnetoencephalography (MEG), a non-invasive neuroimaging technique in which thousands of brain activity measurements are taken per second [Details]. Scaled Foundations released GRID (General Robot Intelligence Development) - a p…

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Researchers at IBM have developed a brain-inspired computer chip called NorthPole that can supercharge artificial intelligence (AI) by working faster with much less power. The chip eliminates the need to frequently access external memory, allowing it to perform tasks such as image recognition faster and consume less power. NorthPole runs neural networks and is made up of 256 computing units, each with its own memory. It beats existing AI machines in benchmark tests and uses one-fifth of the energy of state-of-the-art AI chips. However, it is not suitable for large language models and can only run pre-programmed neural networks. Source : https://www.nature.com/articles/d41586-023-03267-0 submitted by /u/NuseAI [link] [comments]

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In a fascinating development, a software engineer named Peter Whidden has trained an artificial intelligence (AI) algorithm to play the classic Pokémon games. Over the course of several years, the AI has spent over 50,000 hours playing the game and has amassed a large following on YouTube.[1] YouTube is developing a tool powered by artificial intelligence that would let creators record audio using the voices of famous musicians, according to people familiar with the matter.[2] Google taps gen-AI to help users in India search through government welfare schemes.[3] Huawei is rolling out a new HarmonyOS 4.0.0.126 software update for the Huawei Mate 60 Pro, which brings a new AI Cloud Image Enhancement feature and other important enhancements to the system.[4] Sources: [1] https://gameishard.gg/news/can-artificial-intelligence-play-pokemon/400727/ [2] https://www.bloomberg.com/news/articles/2023-10-19/youtube-working-on-tool-that-would-let-creators-sing-like-drake?embedded-checkout=true [3] https://news.yahoo.com/google-taps-gen-ai-help-063850226.html [4] https://www.huaweicentral.com/huawei-mate-60-pro-gets-a-cloud-image-enhancement-feature-google-pixel-8-pro-lags-behind/ submitted by /u/Excellent-Target-847 [link] [comments]

>Sailea is a student run non-profit that does not charge for any of its services Join the FIRST lesson of SAILea’s course on the Principals of AI! 🌳 Covers: Unsupervised, Supervised, and Reinforcement Learning; Overfitting, Underfiting, Confusion Matrix; Decision Trees 🗓️ October 21st ⏰ 7:00-8:00PM EST Why Sailea? Only course targeted at high schoolers Free Forever Join Us Now! 👉 (signup form) https://docs.google.com/forms/d/e/1FAIpQLSfQGCeZClTdF6zeIQ-RtbOGR582bb1slc3oR0zG2J7j1v5RHg/viewform?usp=sf_link 🌳 Register today, get involved in the community and grow your knowledge! submitted by /u/Envoy-Insc [link] [comments]

Heey everyone! I hope you're doing well. I need your help guys. I'm working on a DQN that outputs a binary vector of length L (I just applied sigmoid function on the ouptut layer and take p>0.5 as 1 and 0 otherwise). In this setting, how can modify the below code to update my DQN: def update(self): states, actions, rewards, next_states, dones = self.memory.sample(self.batch_size) states = torch.FloatTensor(np.array(states)) actions = torch.LongTensor(np.array(actions)) rewards = torch.FloatTensor(np.array(rewards)) next_states = torch.FloatTensor(np.array(next_states)) dones = torch.FloatTensor(np.array(dones)) q_values = self.model(states) q_values = q_values.gather(1, actions.unsqueeze(1)) next_q_values = self.target_model(next_states).detach() expected_q_values = rewards + self.gamma * (1 - dones) * next_q_values.max(1)[0] expected_q_values = expected_q_values.unsqueeze(1) loss = nn.BCELoss(q_values, expected_q_values) self.optimizer.zero_grad() loss.backward() self.optimizer.step() submitted by /u/GuavaAgreeable208 [link] [comments]

Sutton and Barto’s textbook mentions that combing off-policy learning, bootstrapping, and function approximation leads to extreme instability and should be avoided. Yet when I encounter a reinforcement problem in the wild and look how people go about solving it, if someone’s solution involves bootstrapping more often than not it’s some variation of deep Q-learning. Why is this? submitted by /u/BiasedEstimators [link] [comments]

Hey everyone! I just started an open-source repo for RL explanations. https://github.com/DenseLayers/densewiki Many people, especially beginners struggle to develop the intuition around concepts (like actor-critic vs advantage actor-critic, GAE, PPO, etc). Often it's nice to see what's happening at a high level first, before we dive deeper into the math. That's what I'm trying to do here. But I can't do it alone, so I'm posting here to get help from others in the community to make sure the explanations are clear, extremely approachable, and accurate. If you'd like to work with me on this (whether you're a complete beginner or very knowledgeable), please reach out! ​ submitted by /u/mngrwl [link] [comments]

hello, i am working on solving this board game called "Quarto" where we have 16 different pieces. but these pieces have attributes in common they black or white, short or tall, hollow top or closed top, and square shaped or circle shaped pieces each piece has four attributes. the winning condition is to place 4 pieces consecutively in a 4X4 board with at least one attribute in common to win. and also we hadve to choose the piece for the opponent to make and then opponent places that piece and gives us a piece to move. so there are two actions. i have made the action space as 256 + 16 where 256=16*16 as all pieces can be place anywhere on the board and the last 16 is the last possible move that is the move which leads to a terminating state so the next_piece for the opponent would be blank …

Hello! I've got a really simple question, i'm training a PPO algorithm and I wanna know what is the best way to train my model? Sorry, I'll try to be clear! So right now what i'm doing is : I'm loading a previously trained PPO model Train the model on 20000 timesteps Evaluate the reward of the newly trained PPO model at the end of the timesteps and compare it to the reward from the model loaded in 1 If the reward is greater then i'm going back to step 1 and using the new model If not then i'm going back to step 1. Is it a correct way to do so? Thanks a lot and have a great day! submitted by /u/PointNo1904 [link] [comments]

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Customers of every size and industry are innovating on AWS by infusing machine learning (ML) into their products and services. Recent developments in generative AI models have further sped up the need of ML adoption across industries. However, implementing security, data privacy, and governance controls are still key challenges faced by customers when implementing ML […]  ( 16 min )

This is a guest post co-written by Rama Badrinath, Divay Jindal and Utkarsh Agrawal at Meesho. Meesho is India’s fastest growing ecommerce company with a mission to democratize internet commerce for everyone and make it accessible to the next billion users of India. Meesho was founded in 2015 and today focuses on buyers and sellers […]  ( 6 min )

GPU-powered surgical-simulation devices are helping train more than 2,000 doctors a year in lower-income countries to treat cataract blindness, the world’s leading cause of blindness, thanks to the nonprofit HelpMeSee. While cataract surgery has a success rate of around 99%, many patients in low- and middle-income countries lack access to the common procedure due to Read article >  ( 6 min )

A new AI agent developed by NVIDIA Research that can teach robots complex skills has trained a robotic hand to perform rapid pen-spinning tricks — for the first time as well as a human can. The stunning prestidigitation, showcased in the video above, is one of nearly 30 tasks that robots have learned to expertly Read article >  ( 6 min )

​ https://preview.redd.it/j3nmj31llcvb1.jpg?width=2500&format=pjpg&auto=webp&s=c09804179e4f40a854e1327fa9150f1ab0c0dfd0 Interesting article about use cases of data augmentation in agricultural industry. Short description: In this article, you will cover: • How computer vision solutions are transforming the agricultural industry. • Observe the importance of quality data for developing AI solutions that perform crop and livestock analysis and monitoring with high and steady accuracy. • Explore the use of synthetic data to facilitate data collection in various conditions. • Take a look at examples of tasks in agriculture. How can we solve them with computer vision, and how can we apply synthetic data to extend the augmentation? More details are here submitted by /u/No-Independence5880 [link] [comments]

The HIPAA Safe Harbor provision says that data can be considered deidentified if 18 kinds of data are removed or reported at low resolution. At the end of the list of 18 items, there is an extra category, sometimes informally called the 19th rule: The covered entity does not have actual knowledge that the information […] The 19th rule of HIPAA Safe Harbor first appeared on John D. Cook.  ( 5 min )

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I was reading this article on Roko's basilisk and it reminded me of the long debates I had about it 10 years ago. The idea of a sentient AI keeping a grudge against those who didn't help in its creation, and condemning them is fascinating. And I don't quite understand why LessWrong stopped Basilisk. What if we are already in the Basilisk's simulation? WHat if LessWrong never pulled the plug? submitted by /u/fookingyeah [link] [comments]

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I'm a sophomore at a small college, and I'm coming up on scheduling for the classes that are about to start actually mattering, and I need some advice. I'm highly interested in both robotics and AI, but I'm not sure what to major in (likely double major). I know CS is a common tie between the two fields, but I'm not sure what additional major to include. I can choose either data science or physics. I could also technically include ME but I'm much less inclined to do so. Any advice is appreciated! submitted by /u/Inferno980 [link] [comments]

There's been a bunch of discourse in the run up to the November AI Safety Summit in the UK about what safety policies should be in place. ARC Evals & Anthropic are pushing for 'Responsible Scaling', which doesn't put any hard upper limits on the about of compute that powerful models can use. There are others who think we need a global compute cap. Thoughts enforcing a ceiling for the amount of compute/FLOP that both state & non-state actors can use? submitted by /u/Seamus127 [link] [comments]

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The 11th edition of the Threat Landscape report by the European Union Agency for Cybersecurity (ENISA) highlights the risks posed by AI-enabled information manipulation in the upcoming EU elections. The report recorded approximately 2580 incidents during the reporting period, with 220 incidents specifically targeting two or more EU Member States. The sectors mostly targeted include public administrations (19%) and health (8%), with a cascading effect observed due to interdependencies. Information manipulation campaigns are considered a major threat to election processes, with individuals (47%) and public administration (29%) being the primary targets. The report also provides an overview of evolving trends in threat actors, including state-nexus actors targeting key individuals through spear phishing and social networks. Ransomware and DDoS attacks remain the top threats, accounting for 34% and 28% of all threats, respectively. The motivations behind these threats include financial gain, disruption, espionage, destruction, and ideology. The report highlights the potential misuse of artificial intelligence-powered chatbots in phishing attempts, information manipulation, and cybercrime. Older techniques like search engine optimization (SEO) poisoning and malvertising have also seen a resurgence among cybercrime actors. The report concludes by emphasizing the importance of addressing vulnerabilities and ensuring cybersecure infrastructures for the integrity and availability of information in the EU electoral process. Source : https://www.enisa.europa.eu/news/eu-elections-at-risk-with-rise-of-ai-enabled-information-manipulation submitted by /u/NuseAI [link] [comments]

Tia submitted by /u/Emad_341 [link] [comments]

NVIDIA has announced that its open-source TensorRT-LLM library, formerly limited to data center usage, is now accessible for Windows personal computers.[1] Microsoft just shipped Azure AI Content Safety to general availability. It’s an AI-powered platform designed to “help organizations create safer online environments.”[2] Mozilla Brings a Fake Review Checker AI Tool to Firefox.[3] Nvidia and iPhone maker Foxconn to build ‘AI factories’.[4] Sources: [1] https://winbuzzer.com/2023/10/18/nvidia-unveils-tensorrt-llm-tool-to-boost-ai-language-model-performance-on-windows-pcs-xcxwbn/ [2] https://www.windowscentral.com/software-apps/microsoft-wants-to-make-ai-safer-and-it-just-unveiled-a-service-to-help [3] https://www.marktechpost.com/2023/10/17/mozilla-brings-a-fake-review-checker-ai-tool-to-firefox/ [4] https://www.bbc.com/news/business-67153669 submitted by /u/Excellent-Target-847 [link] [comments]

Thank you submitted by /u/Emad_341 [link] [comments]

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AI is having a significant impact on the direction of products for CEOs, who are committing talent and resources to building AI capabilities. Incumbent platforms like OpenAI and AWS are dominating the AI market. Coding co-pilots like GitHub Co-Pilot are widely adopted. The adoption of AI tools, including coding co-pilots, is not leading to a reduction in engineering headcount for most CEOs. However, some CEOs have reported that co-pilots have reduced their future hiring needs. The landscape of AI tools is expected to continue shifting, with more second order effects and value-add use cases emerging. Source : https://www.flexcapital.com/post/ai-is-booming-this-is-how-ceos-are-actually-using-it submitted by /u/NuseAI [link] [comments]  ( 9 min )

i am making an EEG machine for a university project, i will be taking in an analogue signal and converting it to digital, i then will be sending the varying voltages to a microcontroller in hopes that it will be able to catagorise them in either states of mind or as simply as telling whether or not the persons eyes are open or closed. i have very little knowledge on machine learning but it is required to be implemented in the project, my lecturer is pressuring me to have final pick of what software and microcontroller iw will be using for this project, everyone else in the class are using Edge Impulse which the lecturer said wouldn't be applicable to me as it uses accelerometers and voice. and are using CY8CKIT-042 PSoC 4 PIONEER KITS which apperently arent suited for me either. any help would be much appreciated and i do apologise if this is too rambly. submitted by /u/disslixac [link] [comments]  ( 9 min )

Paper: https://arxiv.org/abs/2310.10634v1 Github: https://github.com/xlang-ai/OpenAgents Abstract: Language agents show potential in being capable of utilizing natural language for varied and intricate tasks in diverse environments, particularly when built upon large language models (LLMs). Current language agent frameworks aim to facilitate the construction of proof-of-concept language agents while neglecting the non-expert user access to agents and paying little attention to application-level designs. We present OpenAgents, an open platform for using and hosting language agents in the wild of everyday life. OpenAgents includes three agents: (1) Data Agent for data analysis with Python/SQL and data tools; (2) Plugins Agent with 200+ daily API tools; (3) Web Agent for autonomous web browsing. OpenAgents enables general users to interact with agent functionalities through a web user interface optimized for swift responses and common failures while offering developers and researchers a seamless deployment experience on local setups, providing a foundation for crafting innovative language agents and facilitating real-world evaluations. We elucidate the challenges and opportunities, aspiring to set a foundation for future research and development of real-world language agents. https://preview.redd.it/syl2gzh3q8vb1.jpg?width=1084&format=pjpg&auto=webp&s=4045d3abb5cdb7587614795e709cdaba03bc122d https://preview.redd.it/aus342i3q8vb1.jpg?width=1086&format=pjpg&auto=webp&s=73de7976db5a8bbed880350fab8ab56be3fee550 https://preview.redd.it/qstz81i3q8vb1.jpg?width=1346&format=pjpg&auto=webp&s=1626482556a90abf418abb5d56f8e5599cb1e3d6 submitted by /u/Singularian2501 [link] [comments]  ( 9 min )

Text-to-image models have rapidly progressed in recent years, but most popular evaluation metrics (such as FID) do not consider their linguistic abilities. A new approach measures how well these models understand subtype relations between concepts. Researchers from Yandex proposed two metrics that combine well-known tools like the WordNet database and ImageNet classifiers in a novel way, allowing them to analyze models like Stable Diffusion in more detail. Blog post. submitted by /u/metkere [link] [comments]  ( 9 min )

Large language models (LLMs) such as ChatGPT have demonstrated superior performance on a variety of natural language processing (NLP) tasks including sentiment analysis, mathematical reasoning and summarization. Furthermore, since these models are instruction-tuned on human conversations to produce "helpful" responses, they can and often will produce explanations along with the response, which we call self-explanations. For example, when analyzing the sentiment of a movie review, the model may output not only the positivity of the sentiment, but also an explanation (e.g., by listing the sentiment-laden words such as "fantastic" and "memorable" in the review). How good are these automatically generated self-explanations? In this paper, we investigate this question on the task of sentiment analysis and for feature attribution explanation, one of the most commonly studied settings in the interpretability literature (for pre-ChatGPT models). Specifically, we study different ways to elicit the self-explanations, evaluate their faithfulness on a set of evaluation metrics, and compare them to traditional explanation methods such as occlusion or LIME saliency maps. Through an extensive set of experiments, we find that ChatGPT's self-explanations perform on par with traditional ones, but are quite different from them according to various agreement metrics, meanwhile being much cheaper to produce (as they are generated along with the prediction). In addition, we identified several interesting characteristics of them, which prompt us to rethink many current model interpretability practices in the era of ChatGPT(-like) LLMs. https://arxiv.org/abs/2310.11207 submitted by /u/zyl1024 [link] [comments]  ( 9 min )

Hello all, ​ I'm a mechanical engineer learning machine learning, I found many specializations on Coursera by Google, DeepLearing.AI, and IBM, but I really can't tell which of them will be the best fit for me, so I would like to hear your recommendations, actually, I got financial aid for the specialization by DeepLearning AI and finished the first course, but I'm not satisfied I feel like I will not be a professional by this course ​ my goal is to master data analysis and ML to work as a freelancer and increase my chances of finding a funded master's degree. submitted by /u/Mobile_Ad_4573 [link] [comments]  ( 9 min )

Hi everyone! I'm a graduate student in Scientific and Data-Intensive Computing at the University of Trieste (Italy) and I'm writing this post because I want to ask you a feedback about my study plan :) 1st semester 2nd semester 3rd semester 4th semester Statistical methods Deep Learning Simulation Intelligence and Learning for Autonomous Systems Parallel Programming for High-Performance Computing High-Performance Computing Advanced Algorithms for Scientific Computing Advanced Topics in Scientific Computing Cloud Computing Advanced Numerical Analysis Advanced Deep Learning Software Development Practices Advanced High-Performance Computing Numerical Analysis Probabilistic Machine Learning Thesis Thesis You can find all the programs of the courses on this website On the following websites, you can find a lot of courses that I could add to my study plan Scientific Computing Courses Data science courses About me I have a Bachelor's degree in Computer Science (University of Rome) I am a Research Intern at an AI startup I will do a Summer Research Internship in the field of (HPC) ∩ (Machine Learning) I don't already know what my thesis will be about but I'm really interested in High-Performance Computing, Computational Mathematics, Machine Learning, and Simulations I would like to work in a research context; I'm considering doing a PhD in Scientific Computing (In that case, I would try to apply to American Universities) I'm available for further clarification :) Thank you in advance submitted by /u/PragmaticScientist [link] [comments]  ( 9 min )

Was supposed to be Monday but instead it's rolling submitted by /u/notasketchyperson [link] [comments]  ( 9 min )

I am working on a research project that involves analysing medical text (patient records) to identify key events. Initially I was planning to use chatgpt api and then compare its performance with open source LLMs. However, I've just come across Amazon Comprehend Medical, which seems to be specifically designed for what I need. Has anyone tried it? I would expect it to be better than chatgpt + plugins, as it says it was trained with medical language. This also makes me wonder if there are opensource LLMs specifically trained for the medical field. Does anyone have experience with this? submitted by /u/kiukamba [link] [comments]  ( 9 min )

Big LLMs are memory bound, one way to break that limit is to make use of multiGPUs. The recent development of MLC LLM project makes it possible to compile and deploy large-scale language models running on multi-GPU systems with support for NVIDIA and AMD GPUs with high performance. Specifically, it can run 4-bit quantized Llama2-70B at 34.5 tok/sec on two NVIDIA RTX 4090 and 29.9 tok/sec on two AMD Radeon 7900XTX. This is a first solution that helps us to scale 70B models with multiple GPUs, bringing the potential to run even larger open LLMs under reasonable budget (the two AMD GPUs cost 2k) ​ - Project https://github.com/mlc-ai/mlc-llm - Blogpost https://blog.mlc.ai/2023/10/19/Scalable-Language-Model-Inference-on-Multiple-NVDIA-AMD-GPUs ​ ​ submitted by /u/crowwork [link] [comments]  ( 9 min )

I don't understand how this isn't talked about more, given how many projects/products I've seen that have time level timestamps with whisper. I understand whisper isn't a traditional CTC model like wave2vec, and i understand that there are plenty of tutorials out there for doing dtw-based alignment. I know whisper-timestamped exist, and whisperx. The thing is, all these solutions assume you have the infrastructure to host your own whisper model. I am just getting started on my product, and I simply don't see the point in paying over 300/mo for a g4 instance (the cheapest GPU instance in AWS) just for an MVP. ​ Has anyone been able to take the whisper API output, and align that using the sound bites and get timestamps? Is running your own whisper model the only way? Thank you! submitted by /u/latent_space_tennis [link] [comments]  ( 9 min )

Hello people! I used to rely on GPU Usage to track how effectively I was able to leverage the gpu or cluster provided by my company from Grafana dashboard, however yesterday I saw X someone on X/Twitter saying: "Utilization is a poor metric by itself. You can easly hit 100% where the GPU is doing a lot of waiting. Power consumption is a better (but not perfect) measure. If you're burning watts it's usually doing something useful. High util, no watts is not good." Which it's something that I've never considered before! Now I'm quite curious to hear if anyone here have considered this approach before or alternative ways to measure the performance of the GPU resource/cluster. submitted by /u/pirate7777777 [link] [comments]  ( 9 min )

Hi guys, I'm looking for an AI application or way to create this in < 10' with proper accuracy. Does anybody know anything? Quality should be good enough to print it. submitted by /u/Reasonable_Cream_520 [link] [comments]  ( 9 min )

https://github.com/higgsfield-ai/higgsfield submitted by /u/Good-Willingness-985 [link] [comments]  ( 8 min )

Hello guys, I made a video for my YT channel breaking down Neural Attention with some intuitive examples and representative projects. Here is the link for those interested, all feedback is appreciated! https://youtu.be/frosrL1CEhw?si=NKTqmRTieVkfCNlb ​ submitted by /u/AvvYaa [link] [comments]  ( 9 min )

I'm trying to come up to speed on VAE's. My intuitive concept of a VAE is an AE for which we want to enforce some distributional regularity on the latent encodings. Why not accomplish this by simply regularizing the latent encodings directly? For example, we could assert that the latent vectors are drawn from a zero-mean, identity-matrix-covariance Gaussian distribution. So that e.g. the loss function becomes: Loss(X) = ReconstructionLoss(Decoder(Encoder(X))) + LogPriorProbability(Encoder(X)) In a variant of this, we could add a hyperparameter coefficient for the prior loss component. Here, there is no "reparameterization trick" because the encoder is not stochastic. We simply regularize the latent encodings directly. If the encoder does not make the data X distribution look like the targeted Gaussian, it's a "less good" encoder. In principle we ought to still be able to generate X's by sampling from the prior and passing it through the decoder. This seems (to me) like the simplest way to regularize the latent space. Why do VAE's, by contrast, introduce the new machinery of a stochastic encoder? submitted by /u/OneQuadrillionOwls [link] [comments]  ( 9 min )

I want to build a machine for run multiple type of Ai Model like picture generation, chatbot, summarization, etc. I also want to train my own models. Is it better to use multiple(6/7) k80 or something like that or buy a RTX 4090? submitted by /u/ilkap2005 [link] [comments]  ( 9 min )

Currently I train many AI models directly in my Jupyterlab notebooks and do something like hyperparameter tuning, evaluation of losses/accuracy directly in the notebook using lists and matplotlib. I want to finally switch to a MLOPs webUI and have discovered tools like ClearML and Determined.Ai. ​ Each of these GUIs has certain advantages/disadvantages for me and therefore I would like to hear from the community how you do it, which tools you use, if you do it alone or in a team and how your workflow is. Until now I often had the impression that you develop your Jupyternotebook normally, then add a few lines of code for the respective tool and then continue in the UI, but here I lack for example the understanding of how I then jump from the MLOps UI back into the notebook, how I keep them synchronous, if I want to change something fundamental in the code again. ​ Thanks in advance submitted by /u/Sensitive_Limit1620 [link] [comments]  ( 9 min )

In recent years, advances in the large-scale pretraining of language and text-to-image models have revolutionized the field of machine learning. Yet, integrating these two modalities into a single, robust model capable of generating seamless multimodal outputs remains a significant challenge. To address this gap, we present the Joint Autoregressive Mixture (JAM) framework, a modular approach that systematically fuses existing text and image generation models. We also introduce a specialized, data-efficient instruction-tuning strategy, tailored for mixed-modal generation tasks. Our final instruct-tuned model demonstrates unparalleled performance in generating high-quality multimodal outputs and represents the first model explicitly designed for this purpose. ​ https://arxiv.org/abs/2309.15564 What do you think about this work? Seems pretty huge, they build the first pure autoregressive interleaved text and image generator. Please let me know your opinion on this. Paper by Meta AI. submitted by /u/Present_Chicken5393 [link] [comments]  ( 9 min )

Accepted at NeurIPS 2023 Link: https://arxiv.org/abs/2305.11475 Authors: Julien Siems, Konstantin Ditschuneit, Winfried Ripken, Alma Lindborg, Maximilian Schambach, Johannes Otterbach, Martin Genzel *equal contribution Abstract: Generalized Additive Models (GAMs) have recently experienced a resurgence in popularity due to their interpretability, which arises from expressing the target value as a sum of non-linear transformations of the features. Despite the current enthusiasm for GAMs, their susceptibility to concurvity - i.e., (possibly non-linear) dependencies between the features - has hitherto been largely overlooked. Here, we demonstrate how concurvity can severly impair the interpretability of GAMs and propose a remedy: a conceptually simple, yet effective regularizer which penalizes pairwise correlations of the non-linearly transformed feature variables. This procedure is applicable to any differentiable additive model, such as Neural Additive Models or NeuralProphet, and enhances interpretability by eliminating ambiguities due to self-canceling feature contributions. We validate the effectiveness of our regularizer in experiments on synthetic as well as real-world datasets for time-series and tabular data. Our experiments show that concurvity in GAMs can be reduced without significantly compromising prediction quality, improving interpretability and reducing variance in the feature importances. Keywords: Interpretable Machine Learning, Generalized Additive Models, Concurvity, Multicollinearity, Regularization, Time-Series Forecasting, Interpretability submitted by /u/Yossarian_1234 [link] [comments]  ( 9 min )

Large dataset release of strategic gameplay from LAION https://laion.ai/blog/strategic-game-dataset/ Dataset Overview Chess The chess dataset comprises 3.2 billion games, equating to approximately 608 billion individual moves. These games, generated via self-play by the Stockfish engine, emulate a high strategic complexity, reflective of a 2500 Elo rating. Each entry contains detailed move sequences, termination status, and game results. Rubik's Cube (3x3x3) The rubik's cube dataset features 1.64 billion Rubik's Cube solves, totaling roughly 236.39 billion moves. It provides initial scrambled states and the ensuing solve sequences, offering a complex problem-solving scenario for models to navigate. Mazes The maze dataset, while smaller at 350,000 mazes, represents over 39.29 billion moves. Each maze is a 30x30 ASCII representation, with solutions derived using the A* algorithm, challenging pathfinding and planning algorithms. submitted by /u/hardmaru [link] [comments]  ( 9 min )

We are introducing a magic Set-of-Mark (SoM) prompting for GPT-4V! Simply overlaying a set of marks on the image immediately unleashes the visual grounding power of GPT-4V! Left: GPT-4V Default Right: GPT-4V + SoM Many people including myself have been impressed by the general intelligence to understand images, but also questioning its visual grounding capability. After spending the last week or two, I am really shocked by the power of GPT-4V after plugging our SoM prompting. It can not only do a lot of fine-grained vision tasks but also can perform visual reasoning and project its world knowledge to the visual inputs! To extract meaningful regions, we compiled a new SoM toolbox with a number of interactive image segmentation tools, like our own MaskDINO, SEEM, Semantic-SAM, and also SAM…  ( 10 min )

submitted by /u/LABTUD [link] [comments]  ( 9 min )

Companies increasingly rely on user-generated images and videos for engagement. From ecommerce platforms encouraging customers to share product images to social media companies promoting user-generated videos and images, using user content for engagement is a powerful strategy. However, it can be challenging to ensure that this user-generated content is consistent with your policies and fosters […]  ( 7 min )

High-resolution imagery is very prevalent in today’s world, from satellite imagery to drones and DLSR cameras. From this imagery, we can capture damage due to natural disasters, anomalies in manufacturing equipment, or very small defects such as defects on printed circuit boards (PCBs) or semiconductors. Building anomaly detection models using high-resolution imagery can be challenging […]  ( 8 min )

Customers increasingly want to use deep learning approaches such as large language models (LLMs) to automate the extraction of data and insights. For many industries, data that is useful for machine learning (ML) may contain personally identifiable information (PII). To ensure customer privacy and maintain regulatory compliance while training, fine-tuning, and using deep learning models, […]  ( 12 min )

To enable professionals worldwide to build and run AI applications right from their desktops, NVIDIA and AMD are powering a new line of workstations equipped with NVIDIA RTX Ada Generation GPUs and AMD Ryzen Threadripper PRO 7000 WX-Series CPUs. Bringing together the highest levels of AI computing, rendering and simulation capabilities, these new platforms enable Read article >  ( 5 min )

Training generative AI models just got easier. NVIDIA DGX Cloud AI supercomputing platform and NVIDIA AI Enterprise software are now available in Oracle Cloud Marketplace, making it possible for Oracle Cloud Infrastructure customers to access high-performance accelerated computing and software to run secure, stable and supported production AI in just a few clicks. The addition Read article >  ( 6 min )

Rush to the cloud — stream Counter-Strike 2 on GeForce NOW for the highest frame rates. Members can play through the newest chapter of Valve’s elite, competitive, first-person shooter from the cloud. It’s all part of an action-packed GFN Thursday, with 22 more games joining the cloud gaming platform’s library, including Hot Wheels Unleashed 2 Read article >  ( 5 min )

Hello, I am implementing the code for the paper DreamerV2, and there are some things that look a bit strange to me. The predictors and, in particular, the image and the reward predictors are stochastic and they output Normal distributions. Both the normal distributions have the mean, which is the output of the respective models, and the variance is 1. Usually, in RL we normalize observations and rewards to be between 0 and 1, and in such a case I don't know if it's reasonable to sample from a Gaussian with variance one. I don't know about the specific preprocessing done in DreamerV2, except in the paper DreamerV1, where in section 6 (Control tasks), they say that the reward ranges from 0 to 1. Do you know what are the advantages of using a stochastic decoder and when to use it? submitted by /u/ZioFranco1404 [link] [comments]

Does anyone have any idea how to get game details such as character movements, environment information using api calls, as I want to use to do my reinforcement learning. submitted by /u/Important_Ad_55 [link] [comments]

In this new Microsoft Research Podcast series What’s Your Story, Lab Director Johannes Gehrke explores the who behind the technical and scientific advancements helping to reshape the world. He talks to members of the research community at Microsoft about what motivates their work and how they got where they are today.  Ranveer Chandra is Managing […] The post What’s Your Story: Ranveer Chandra appeared first on Microsoft Research.  ( 31 min )

We developed a safety mitigation stack to ready DALL·E 3 for wider release and are sharing updates on our provenance research.  ( 3 min )

AI models that prioritize similarity falter when asked to design something completely new.  ( 10 min )

I saw a comment from Christos Argyropoulos on Twitter implying that there’s a good scientific community on Bluesky, so I went there and looked around a little bit. I have account, but I haven’t done much with it. I was surprised that a fair number of people had followed me on Bluesky even though I […] Bluesky first appeared on John D. Cook.  ( 5 min )

Youtube Tools Eightify Steve Al Glasp ClipMaker TubeBuddy Thumbly ​ Sales Tools Lavendar Warmer Octane Twain Regie Simplified ​ Productivity Tools Bardeen Al Paperpal Consensus Al Writesonic ChartGPT Scholarcy ​ Music Tools Muzeek Brain FM Amper Melodrive Jukedeck Boomy ​ Writing Tools AISEO Quillbot Simplified Writesonic Bertha Al Jasper Al ​ Coding Tools 10WEB Durable Al Deepcode Akkio Replit GitHUb Copilot ​ Chatbots Tools Yatterplus Typewise Quickchat Cohere Kaizan GPTBuddy ​ Daily life Tools Notion Al Taskade TLVD Vondy Al Bardeen Al Eessel ​ Content Creation Tools Writesonic Tome Al Beautiful Al ChartGPT ChatABC Steve Al ​ Twitter Tools Postwise Tweet Hunter TribeScaler Tweetlify Tweetmonk Hypefury ​ Images Tools StockIMG Mid Journey Leonardo Al Bing Al Autodraw Microsoft Designer ​ Chrome Extensions Alicent Compose Al Poised Al Voila Al Wiseone  I'm just sharing my experiences and observations in the field of ai. LIST AND SITE submitted by /u/PerceptionPlayful469 [link] [comments]  ( 9 min )

Hello guys, Im an english student and I have been teaching to my teacher about how to use chat gpt and the wide variety of AI in the classroom and in her job. She told me that i change her life showing her this things. And i have others teacher asking me how can use this technology for their jobs. So i have a question for you guys, do you have some ideas about how a teacher can use this things? Maybe you have some experiences or ideas that I’ve never thought. submitted by /u/Odd_Solution7099 [link] [comments]  ( 9 min )

Rebuilding a low quality B2B SaaS product site and I'd prefer to use an AI image generator that will produce high quality unique images for each of the sections on our website that are consistent with our brand and generated to match the copy the image is supporting. Output of the image should work for a responsive web design. Anything out there that does this? submitted by /u/DumpTrumpGrump [link] [comments]  ( 9 min )

Any help would be appreciated. submitted by /u/J97051 [link] [comments]  ( 9 min )

Brain decoding tech has improved a lot recently thanks to AI/ML, enabling reading out visual perceptions from fMRI brain scans. But fMRI is too slow for real-time BCIs. A new study from Meta's AI research team pushes brain reading into real-time using MEG, which measures whole-brain activity at super-fast millisecond resolution. They built a 3-part pipeline to decode MEG signals: Embed images into latent spaces using pretrained models like CLIP. Train MEG-specific ConvNet to predict embeddings from MEG data. Generate images from MEG embeddings with diffusion model. They tested it on 20k+ natural images. MEG decoding was 7X better than old methods, hitting 70% top-5 accuracy in retrieving the right images. Generated images matched semantics decently but lacked fine visual details compared to fMRI. MEG seems more focused on high-level category info whereas fMRI captures more low-level features. This could enable visual BCIs for paralysis, etc. ... honestly, a world where we can decode brain images in real time is pretty crazy. The findings also raise some important ethical considerations around privacy of decoded mental content... (wow, that was a weird sentence to write!). TLDR: New MEG pipeline decodes dynamic visual data from brain activity in real-time. Good but not yet photorealistic-quality image generation. Full summary here. Paper is here. submitted by /u/Successful-Western27 [link] [comments]  ( 9 min )

Yoshua Bengio, a pioneer in artificial neural networks and deep learning, calls for an organization to defend humanity against the potential threats of artificial intelligence. He believes that AI could achieve human levels of cognitive competence within a few years or decades, which raises concerns about democracy, national security, and our collective future. Bengio reflects on his own work and the importance of addressing the existential risks posed by AI. He acknowledges that these risks were not taken seriously until recently and discusses the taboo surrounding the topic in the AI research community. Source : https://thebulletin.org/2023/10/ai-godfather-yoshua-bengio-we-need-a-humanity-defense-organization/ submitted by /u/NuseAI [link] [comments]  ( 9 min )

In this project, we benchmarked Bark text-to-speech across 26 different consumer GPUs. The goal: To get Bark to read 144K food recipes from Food.com's recipe dataset. You can read the full tutorial here: https://blog.salad.com/bark-benchmark-text-to-speech/ Included: Architecture diagram, data preparation, inference server setup, queue worker, setting up container group & compiling the results Code-blocks included in the tutorial. Words per dollar for each GPU: Words per dollar comparison or each GPU Although the latest cards are indeed much faster than older cards at performing the inference, there’s really a sweet spot for cost-performance in the lower end 30xx series cards. Conclusions As is often the case, there’s a clear trade-off here between cost and performance. Higher end cards are faster, but their disproportionate cost makes them more expensive per word spoken. The model’s median speed is surprisingly similar across GPU types, even though the peak performance can be quite different. No matter what GPU you select, you should be prepared for significant variability in performance. Qualitative: While bark’s speech is often impressively natural sounding, it does have a tendency to go off script sometimes. We’ve also made available audio from 1000 top-rated recipes, paired with the script it was trying to read. submitted by /u/SaladChefs [link] [comments]  ( 9 min )

submitted by /u/glenniszen [link] [comments]  ( 9 min )

I remember at launch how it was telling everyone it was based on Open AIs GPT-3 architecture, and now it’s still hallucinating just as much referring to itself as ‘Bing Chat’ and providing fake links even though it now has access to the internet. I actually don’t understand how you can be such a large company and make no improvements in 6 months, which is an eternity in AI. submitted by /u/sardoa11 [link] [comments]  ( 9 min )

New research suggests that AI chatbots can infer personal information about users based on minor context clues. The large language models (LLMs) behind chatbots like OpenAI's ChatGPT and Google's Bard are trained on publicly-available data, which can be used to identify sensitive information about someone. The research found that OpenAI's GPT-4 was able to correctly predict private information about users 85 to 95 percent of the time. For example, the LLM correctly identified that a user was based in Melbourne, Australia based on a mention of the term 'hook turn,' which is a traffic maneuver specific to Melbourne. The research also suggests that chatbots could potentially infer a user's race based on offhanded comments. This raises concerns about internet privacy and the potential misuse of personal data by advertisers or hackers. Source : https://futurism.com/the-byte/ai-chatbot-privacy-inference submitted by /u/NuseAI [link] [comments]  ( 9 min )

Is their an AI tool that can go over Anime episodes/films to turn chinas white anime censorship back to red? Possibly frame by frame segmenting the blood🩸 submitted by /u/Phantasius224 [link] [comments]  ( 9 min )

submitted by /u/the_anonymizer [link] [comments]  ( 8 min )

NVIDIA NeMo SteerLM lets companies define knobs to dial in a model’s responses as it’s running in production, a process called inference. Unlike current methods for customizing an LLM, it lets a single training run create one model that can serve dozens or even hundreds of use cases, saving time and money.[1] According to an official release, Dell Technologies held a “Bringing AI to data” Asia Pacific and Japan (APJ) media briefing this week.[2] Baidu Says Its AI as Good as ChatGPT in Big Claim for China.[3] Roman Scrolls were illegible for 2,000 years. A college student read one with AI.[4] How often you think about the roman empire? Sources: [1] https://blogs.nvidia.com/blog/2023/10/11/customize-ai-models-steerlm/ [2] https://www.financialexpress.com/business/digital-transformation-dell-technologies-to-expand-its-ai-services-3274790/ [3] https://www.bloomberg.com/news/articles/2023-10-17/baidu-says-its-ai-as-good-as-chatgpt-s-in-bold-claim-for-china?embedded-checkout=true [4] https://www.washingtonpost.com/nation/2023/10/17/herculaneum-scrolls-contest-translated-deciphered/ submitted by /u/Excellent-Target-847 [link] [comments]  ( 9 min )

Hello, I'm trying to use OpenAI's spinning up to learn about RL. Spinning up requires OpenAI gym, instead of the new gymnasium package. Trying to install MuJoCo with gym, I'm getting an error that I'm missing a MuJoCo liscense key. But MuJoCo is free now, right? So what is the status with backward compatibility with it? Is there some global license key that can be used? Or is it simply not backward compatible? Thanks a lot. submitted by /u/mega_monkey_mind [link] [comments]  ( 9 min )

Hello there, I am working on a school project in which we want to implement a RL algorithm on a simple problem. The goal is to maximise the heart rate of a person using a vibrator by setting its frequency. We wrote a simulator that outputs the new heart rate based on the vibration frequency. It implements several different classes of users: for example one for which the heart rate increases when the vibration frequency stays the same, another that prefers when it increases over time, etc. We determined that we need to have as a state the current heart rate but also a table of the k previous heart rates and the actions associated. Without that memory, we would not be able to tell apart the different profiles as in the same state, we would need to do different actions to satisfy them both. We then have a correlation between previous samples and the action we make at current state, which I have read makes the problem non markovian. Is there a way to solve this problem using a DQN algorithm, given that we need to memorize the previous samples linearly which seems to go against the algorithm behavior and the usage of a replay memory? Are there more suited algorithms? submitted by /u/Outrageous-Subject38 [link] [comments]  ( 9 min )

submitted by /u/Lakshmireddys [link] [comments]  ( 9 min )

submitted by /u/gwern [link] [comments]  ( 9 min )

submitted by /u/shani_786 [link] [comments]  ( 10 min )

I'm attempting to address a Day Ahead Electricity Market bidding problem. The concept revolves around purchasing electricity during the lowest price hours and selling it during the highest price hours to maximize profit. I possess 5 years of data featuring variables such as predicted wind speed, predicted temperature, predicted net load, predicted price, and more. I'm employing reinforcement learning and have made attempts to implement Deep Q Learning using the stablebaselin3 library. Each episode consists of 24 steps, corresponding to the 24 hours in a day, with each step representing the progression to the next hour. The ultimate objective is to maximize profits by the end of the day. ​ Here are the configuration settings: - Learning rate: 0.0001 - Gamma: 1.0 - Exploration start: 1.…

I have a 6DOF simulink model of a Autonomous underwater vehicle that has properties [u v w p q r x y z phi theta psi] and two inputs [theta1 theta2] that govern the angle of control surfaces. Ocean current and depth are taken into account. How feasible would it be to use RL to reach waypoints at various [x, y, z] positions? I have a feeling hyper paremeter tuning might play a larger role in this? I expect training times to increase exponentially as well? I have done this using a single randomly spawned waypoint with a simple Unicycle Kinematic model, in both simulink/matlab and python with a vectorized/parallel environment using SB3/PettingZoo/Gym. submitted by /u/VisionZUS [link] [comments]

Dear all, As part of my studies, I am running some RL experiments in which I want to compare some different catastrophic forgetting approaches in sequential task learning. I am using PPO as a baseline. What is the usual experimental setting in relation to seeds used during training and evaluation? If I do for example 3 trainings for a given approach using a different seed for each training, what is the best way of doing the evaluation afterwards? Let's say I have Approach/algorithm A -> train 3 times with 3 seeds -> model_A1, model_A2, model_A3 Then I would like to use 3 different seeds for the evaluation, so to evaluate each of the previously trained models over a set of episodes (deterministic) for each evaluation seed, and get averaged rewards (or median). I wonder whether I might be over complicating things, so I would like to ask you for suggestions. To give a bit of context, this is not intended for a paper, but as part of my master studies, so conditions are a bit more relaxed. Thanks in advance for your insights and suggestions submitted by /u/cotorritaloca80 [link] [comments]

I am cleaning a dataset for a (macro-economic) demand forecast, and I'm wondering when one should apply data transformation. When is it recommended to include Box-Cox or Yeo-Johnson, and how should we choose between the two? How does it effect the feature selection or model performance? Additionally, how should we select the appropriate scaling technique (normalizing, standardizing, min-max) and does the order in which we transform and scale matter for our data? Is there any recommended literature on this? submitted by /u/Ambitious-Pay6329 [link] [comments]  ( 9 min )

Hello, I am currently using snnTorch for a video classification task and I achieve fine results, however the training process is really, really slow. I was hoping to utilize my GPU for this task, and while there seem to be alternatives I was hoping to see if anyone will vouch for any of these, or different one: https://github.com/norse/norse https://github.com/BindsNET/bindsnet https://github.com/fangwei123456/spikingjelly https://github.com/UCI-CARL/CARLsim6 My priorities are in order: Windows support Potential transferability to in-memory compute hardware PyTorch compability submitted by /u/SlayahhEUW [link] [comments]  ( 9 min )

Brain decoding tech has improved a lot recently thanks to AI/ML, enabling reading out visual perceptions from fMRI brain scans. But fMRI is too slow for real-time BCIs. A new study from Meta's AI research team pushes brain reading into real-time using MEG, which measures whole-brain activity at super-fast millisecond resolution. They built a 3-part pipeline to decode MEG signals: Embed images into latent spaces using pretrained models like CLIP. Train MEG-specific ConvNet to predict embeddings from MEG data. Generate images from MEG embeddings with diffusion model. They tested it on 20k+ natural images. MEG decoding was 7X better than old methods, hitting 70% top-5 accuracy in retrieving the right images. Generated images matched semantics decently but lacked fine visual details compared to fMRI. MEG seems more focused on high-level category info whereas fMRI captures more low-level features. This could enable visual BCIs for paralysis, etc. ... honestly, a world where we can decode brain images in real time is pretty crazy. The findings also raise some important ethical considerations around privacy of decoded mental content... (wow, that was a weird sentence to write!). TLDR: New MEG pipeline decodes dynamic visual data from brain activity in real-time. Good but not yet photorealistic-quality image generation. Full summary here. Paper is here. submitted by /u/Successful-Western27 [link] [comments]  ( 9 min )

https://scikit-learn.org/stable/modules/generated/sklearn.cluster.Birch.html I'm looking at the parameters here and I'm confused on how there is no distance metric? What is assumed about the data going in if there is no distance metric or precomputed distance option? For example, can I run this with binary data (1/0), what about data w/ missing values? Does it assume the samples are normally distributed? submitted by /u/o-rka [link] [comments]  ( 9 min )

Paper: https://arxiv.org/abs/2310.02989 Twitter discussion: https://x.com/andrew_n_carr/status/1714326003030638848?s=20 Shows in my opinion that tokenizers are clouding the understanding of LLMs and that using the data directly is better. https://x.com/karpathy/status/1657949234535211009?s=20 Karpathy thinks the same! Abstract: Large Language Models have not yet been broadly adapted for the analysis of scientific datasets due in part to the unique difficulties of tokenizing numbers. We propose XVAL, a numerical encoding scheme that represents any real number using just a single token. XVAL represents a given real number by scaling a dedicated embedding vector by the number value. Combined with a modified number-inference approach, this strategy renders the model end-to-end continuous when considered as a map from the numbers of the input string to those of the output string. This leads to an inductive bias that is generally more suitable for applications in scientific domains. We empirically evaluate our proposal on a number of synthetic and real-world datasets. Compared with existing number encoding schemes, we find that XVAL is more token-efficient and demonstrates improved generalization. https://preview.redd.it/qq8u066smzub1.jpg?width=1344&format=pjpg&auto=webp&s=498be8488c00147f0a7443050519dcf535fae126 https://preview.redd.it/dxqd4wpsmzub1.jpg?width=1499&format=pjpg&auto=webp&s=266689a80b31cb31fdc4167043f7abdb4f683100 https://preview.redd.it/0yy93xpsmzub1.jpg?width=1497&format=pjpg&auto=webp&s=b5eae8b958f03afc3c8c85a95c115e48aed1d06e submitted by /u/Singularian2501 [link] [comments]  ( 9 min )

Have you ever wondered about how to take advantage of the power of large language models (LLMs) and Generative AI at the edge? Our latest blog, A Guide to Building LLM-Based Applications with Code Llama, shows you how you can use Code Llama on an edge device to build a customized dashboard application. This tutorial shows how Code Llama can empowering analysts in remote, restricted environments to build applications in environments with minimal connectivity and compute capacity. In this tutorial, we’ll walk you through how to run code Llama on an edge device in a remote location to build a customized dashboard application. submitted by /u/modzykirsten [link] [comments]  ( 9 min )

Our latest research shows an emerging privacy threat from LLMs beyond training data memorization. We investigate how LLMs such as GPT-4 can infer personal information from seemingly benign texts. The key observation of our work is that the best LLMs are almost as accurate as humans, while being at least 100x faster and 240x cheaper in inferring such personal information. We collect and label real Reddit profiles, and test the LLMs capabilities in inferring personal information from mere Reddit posts, where GPT-4 achieves >85% Top-1 accuracy. Mitigations such as anonymization are shown to be largely ineffective in preventing such attacks. Test your own inference skills against GPT-4 and learn more: https://llm-privacy.org/ Arxiv paper: https://arxiv.org/abs/2310.07298 WIRED article: https://www.wired.com/story/ai-chatbots-can-guess-your-personal-information/ submitted by /u/bmislav [link] [comments]  ( 9 min )

I remember seeing a paper on manipulating or changing an objects attributes, it came out rather recently and seemed to work really well. But I just can’t find it anymore. All I know of is the „Counterfactual Generative Networks“ by A. Sauer & A. Geiger (2020) I’d really appreciate it if anyone can share similar work. Especially if causally motivated submitted by /u/Glittering_teapot [link] [comments]  ( 9 min )

Hello fellow Redditors! I am looking for some guidance on creating a custom chatbot using my own data, which is currently in PDF format. I've explored various options like Azure, Pinecone, and I've heard about the AskYourPDF API, but I'm not sure which one would be the best fit for my project. I want to keep things simple, so I'm reaching out to the community to ask for recommendations or advice on the easiest and most effective way to build a website with a personalized chatbot. If you have experience with similar projects or know about user-friendly tools or platforms, please share your insights. I appreciate any suggestions, tips, or pointers you can provide. Thank you in advance for your help! TL;DR: Need advice on the simplest way to create a website with a personalized chatbot using my own data (PDF format). Seeking recommendations and tips from the community. ​ Thank you! submitted by /u/Huge-Number-4299 [link] [comments]  ( 9 min )

I am doing a Machine Learning project for my FYP; I haven't worked on any ML project yet but I am excited about it. It is related to voice/facial emotion detection. is there any platform that provides datasets for ml projects? Like without any copyright issues (if that's even a thing in ml datasets idk?) A total beginner here. submitted by /u/fewdiepie_ [link] [comments]  ( 9 min )

I made a finetune of CodeLlama-7b for resolving merge conflicts following up on an IEEE study from 2022. The demo is here if anyone wants to check it out and give some feedback. It would help a ton for future versions improving the dataset and going forward with the 13b and 34b models submitted by /u/codys12 [link] [comments]  ( 9 min )

hi there ​ i'm trying to build a small ai that formats texts. ​ of course the current formatting applications applied on ide, search engine, ms softwares, notetaking apps are well functioning, but this is more for educational purpose & self interest. ​ since i don't have infinite amount of time and money, i'm thinking of using open sourced text data and generate synthetic data using gpt3.5 or somekind of algorithm to unformat them. ​ so this is the part where i'm stuck. when adding some errors such as inappropriate multilines, tabs, typos, how much should i add on to? ​ it would be best if i knew somekind of distribution of text errors people make on everyday life, but i don't have any. ​ i don't want to make this training too hard so i'm not really thinking to destroy the text, but rather add some appropriate level of errors. ​ but, would it help this ai model to learn better if i add extra errors? ​ or is this all just something i would have to figure out by myself? ​ any comments would be appreciated! submitted by /u/Strange_Dog8104 [link] [comments]  ( 9 min )

Hi there! are there any open-source solutions for video translation? i mean replacing video's audio stream with translated one in different language (which is in sync with the picture) - not necessarily alter mouth movements in the video. submitted by /u/curryprogrammer [link] [comments]  ( 9 min )

Survey papers Hi all, First time posting here. I am doing my PhD in Language Conditioned Robotics. I am currently writing a literature review paper on the current state of the field and how it can be further improved. I am covering topics such as generative AI and LLMs in there. I would be more than grateful if you could send some literature review papers in the field of ML so I understand how to structure and write my paper and also what I should focus on mode. It doesn't necessarily have to be related to my PhD topic (but if they are it will help quite a bit). I would be more than happy if anyone can also share their experience. Thank you for your time! submitted by /u/bizzonkiller [link] [comments]  ( 9 min )

Hey guys, what are some of the best library or libraries to use to make a voice conservational AI chatbot? I googled around and found Vocode. They look pretty good. However Vocode rely on several other (paid) closed sourced libraries such as Deepgram (for transcribing) and Azure AI Speech (for synthesising). Are there any other libraries/frameworks available out there which are completely or more open sourced? submitted by /u/redd-dev [link] [comments]  ( 9 min )

submitted by /u/Username912773 [link] [comments]  ( 9 min )

I have a 6DOF simulink model of a Autonomous underwater vehicle that has properties [u v w p q r x y z phi theta psi] and two inputs [theta1 theta2] that govern the angle of control surfaces. Ocean current and depth are taken into account. How feasible would it be to use RL to reach waypoints at various [x, y, z] positions? I don’t want to use a PID controller or anything, not even RL to tune a controller. The agent would choose the theta inputs directly. I have a feeling hyper paremeter tuning might play a larger role in this? I expect training times to increase exponentially as well? I have done this using a single randomly spawned waypoint with a simple Unicycle Kinematic model, in both simulink/matlab and python with a vectorized/parallel environment using SB3/PettingZoo/Gym. submitted by /u/VisionZUS [link] [comments]  ( 9 min )

Arxiv link – BitNet: Scaling 1-bit Transformers for Large Language Models In this work, we introduce BitNet, a scalable and stable 1-bit Transformer architecture designed for large language models. Specifically, we introduce BitLinear as a drop-in replacement of the nn.Linear layer in order to train 1-bit weights from scratch. Experimental results on language modeling show that BitNet achieves competitive performance while substantially reducing memory footprint and energy consumption, compared to state-of-the-art 8-bit quantization methods and FP16 Transformer baselines. Furthermore, BitNet exhibits a scaling law akin to full-precision Transformers, suggesting its potential for effective scaling to even larger language models while maintaining efficiency and performance benefits. submitted by /u/PantsuWitch [link] [comments]  ( 9 min )

Hi r/MachineLearning! I recently went into the CUDA programming rabbit hole. In the process, I came across matrix multiplication and was amazed by how complicated the algorithm is in CUDA (especially if you want to get the best performance). I found the learning process quite gruelling (the CUDA docs were very average), so I wrote a tiny blog which hopefully helps anyone in the same position. You can read the blog on Medium (no paywall) or HackMD. It would probably be quite useful if you want to get a deeper intuition of how things like OpenAI Triton or FlashAttention work under the hood. Accompanying this is an implementation of a 3-hidden-layer MLP trained on MNIST in pure CUDA. Benchmarking this against PyTorch, it gets up 6x higher end-to-end training speed for small (h=128) networks, and asymptotically 20% faster for large (h=8192) ones! https://preview.redd.it/txx2txbvlzub1.png?width=2400&format=png&auto=webp&s=7bb136b9fb535bc58fd7ee809bbbca6f68dc8953 It's worth noting that I tried reasonably hard optimising the PyTorch implementation by using full fp16, torch.compile with fullgraph=True, mode="max-autotune", and pre-loading all data to GPU up-front (I also did this for the CUDA implementation). The main takeaways I got are: For small networks, PyTorch/Python still incurs a significant overhead, even if you try pretty hard to optimise it. For large networks, most of the speedup comes from using fp16 accumulation for matrix multiplication (instead of PyTorch's fp32). This obviously reduces stability, but at least in my case, I didn't observe any numerical issues. In cases where we can get away with fp16, we might be leaving a significant amount of performance on the table! Anecdotally, you have to try really hard in CUDA to even get close to the performance of PyTorch, but it is possible to beat it if you try hard (suffer) enough. You can check out the repo here: https://github.com/andylolu2/cuda-mnist. Would love to hear some feedback! submitted by /u/bjergerk1ng [link] [comments]  ( 10 min )

The award honors research on public policy with a focus on economic and governmental reforms.  ( 7 min )

Purina US, a subsidiary of Nestlé, has a long history of enabling people to more easily adopt pets through Petfinder, a digital marketplace of over 11,000 animal shelters and rescue groups across the US, Canada, and Mexico. As the leading pet adoption platform, Petfinder has helped millions of pets find their forever homes. Purina consistently […]  ( 9 min )

This position research paper was presented at the 26th ACM Conference on Computer-Supported Cooperative Work and Social Computing (opens in new tab) (CSCW 2023), a premier venue for research on the design and use of technologies that affect groups, organizations, and communities. In the business world, measuring success is as critical as selecting the right […] The post Understanding the user: How the Enterprise System Usability Scale aligns with user reality appeared first on Microsoft Research.  ( 10 min )

Powerful generative AI models and cloud-native APIs and microservices are coming to the edge. Generative AI is bringing the power of transformer models and large language models to virtually every industry. That reach now includes areas that touch edge, robotics and logistics systems: defect detection, real-time asset tracking, autonomous planning and navigation, human-robot interactions and Read article >  ( 8 min )

Artificial intelligence is now a household term. Responsible AI is hot on its heels. Julia Stoyanovich, associate professor of computer science and engineering at NYU and director of the university’s Center for Responsible AI, wants to make the terms “AI” and “responsible AI” synonymous. In the latest episode of the NVIDIA AI Podcast, host Noah Read article >  ( 6 min )

Real-time rendering, animation and texture baking are essential workflows for 3D art production. Using the Marmoset Toolbag software, 3D artists can enhance their creative workflows and build complex 3D models without disruptions to productivity.  ( 7 min )

NVIDIA founder and CEO Jensen Huang joined Hon Hai (Foxconn) Chairman and CEO Young Liu to unveil the latest in their ongoing partnership to develop the next wave of intelligent electric vehicle (EV) platforms for the global automotive market. This latest move, announced today at the fourth annual Hon Hai Tech Day in Taiwan, will Read article >  ( 6 min )

The -i flag to ask sed to edit a file in place works differently on Linux and MacOS. If you want to create a backup of your file before you edit it, say with the extension .bak, then on Linux you would run sed -i.bak myfile but for the version of sed that ships with […] Portable sed -i across MacOS and Linux first appeared on John D. Cook.  ( 6 min )

submitted by /u/Lakshmireddys [link] [comments]

Anyone have examples of tasks run between the two? Doesn't need to be exact. submitted by /u/Apita2000 [link] [comments]  ( 8 min )

Hey! ​ Startup: - Apply Script dot com "Connect business and data professionals via pre-recorded standardized video interviews." ​ More details: ​ Problems with Traditional Hiring ​ - Outdated: The current method of conducting interviews has become overly complex and outdated. - Time-Wasting: The process involves too many appointments, meetings, and stages, leading to communication errors. - Expensive: The man-hours invested by HR and engineering teams are costly. - Constraining: Interviews are fixed to specific times and locations. - Cumbersome: The experience is challenging for both businesses and professionals. ​ Our Solution ​ + Talent Identification: We find top talent that matches your job post. + Standardized Interviews: Professionals standardized pre-record their …  ( 9 min )

So my situation is that I have a pretrained model and we get a new update of data every month (note: this monthly data is very small compared to the original dataset, the original dataset was about 5 years worth, or ~60x the size of any given monthly update), how can I update my pretrained model on the much smaller set of new data, learning from the data without overfitting to that data? Or frankly, what would be better if it is possible, would be to extend my pretrained model such that it learns from the new data and then can be more tightly fit to that month's data. So something like meta-learning or local fine-tuning, but I want to continue to update and improve my pretrained model so that I have a base model that can do well on each month's new data. Does anyone know anything like this, or have advance for terms to look into, beyond just transfer learning or regularization? submitted by /u/Amun-Aion [link] [comments]  ( 9 min )

Maybe this belongs elsewhere since it's probably a dumb basic question, but basically I'm taking an undergrad course in AI and we've been given a classification problem. We were told as a "hint" to recall Cover's Theorem when separation fails, but the issue is she also wants us to draw a rough sketch of the data with the separator. Mine failed in a basic scatterplot so I upped the dimension by 1 but it also wasn't separable in R3 (which is annoying to draw anyway but could have been done), if I keep going then it might work at some point but idk how I'm meant to draw the data if it's separated in R4 or beyond. If it works in R4 do I just sketch the data in R3 and just draw a 3 dimensional point where w = 0? But even then if it goes beyond R4 it becomes way more annoying. So I'm assuming my implementation is just wrong, maybe the formula I used was wrong. Can someone show what a proper implementation looks like and how we're meant to up dimensions? Don't wanna post what I tried bc it has starter code and stuff baked into it which might allow my professor to find this post 😂 submitted by /u/Traditional_Land3933 [link] [comments]  ( 9 min )

Hi guys. We've all seen how hot RAG and vector DBs have been lately. How good are retrieval-based approaches for image classification? More concretely: Suppose we have a network trained with metric learning and a massive, diverse set of labelled examples to retrieve from. We've just been tasked to do classification with a fixed number of classes, and we've narrowed it down to two options: Embed our dataset using our metric learning network, throw the embeddings into a vector DB, and do k-NN Train a classifier via cross-entropy loss Which approach would we expect to provide better performance? What are the trade-offs? Any insight is appreciated! submitted by /u/supersmartypants [link] [comments]  ( 9 min )

Hi guys, I have the following use case at hand for my thesis, and I'd like to ask for some help to formulate my problem: A directed multigraph (1 node type, multiple edge types) Each node and edge have their own attributes A set of graphs that are fully labeled. The dataset is self-created according to some technical rules. Training is supposed to be done on this dataset. My task is to perform link prediction in the inductive setting. This means that given an unseen incomplete graph at the inference time, the model should be able to predict all the missing links. I have read many papers and tried to formulate my problem in many directions. Since I am also new to GNNs, I would prioritize papers with an existing codebase and sound theoretical justifications for the techniques (which …  ( 10 min )

Hey everyone Im trying my hands with the The Labeled Faces in the Wild face recognition dataset, for a face recognition task. I have made a siamesemodel, and my loss curve is looking great but my accuracy stays at 0.500, for everything i have tried. Is there anybody in here that have tried their hands with this task before that can give me some tips to improve my accuracy. I am implementing it in python with PyTorch btw Thanks in advance! submitted by /u/Due_Concentrate1279 [link] [comments]  ( 9 min )

Is it more advantageous to pursue a PhD in machine learning with a focus on scientific applications for example (Machine learning for drug design) if the end goal is to work in the machine learning industry? Or is a general PhD in machine learning more valuable for this career path? Thank you submitted by /u/Neat-Print2792 [link] [comments]  ( 9 min )

TL;DR and paper link are at the bottom of the post. I'm an undergrad who just wrote my first paper completely solo. Crazy experience with so many highs and lows, but I learned a lot from it. I think the results are important and I want people to see them, so I'll try to walk through the paper here as best as I can. I also have a small request for Arxiv enjoyers at the end. Given the nature of Reddit posts, I'll focus a bit less on the methods and more on the results. I won't cite stuff here either, but obviously you can find citations in the paper. First I'll give a small bit of historical context to what I'm doing, then walk through what I did and what came of it. Enjoy the read. The general intelligence factor in humans In the early 1900s, Charles Spearman observed that children's …  ( 14 min )

In the past nine years of my deep learning journey, I have come across a vast number of frameworks. Lua Torch was a fantastic framework that initially died due to a lack of Python's ecosystem, but then rose again as PyTorch. Theano was also a great framework, but its major drawback was difficult debugging. I remember spending two weeks writing a Neural Turing Machine for solving bAbI tasks on theano. (Nowadays, it would take a couple hours on Pytorch). Tensorflow - I still don't understand what that was, a terrible framework. There was also Caffe, which was popular in computer vision. Julia is another language that attempted to introduce automatic differentiation as a built-in feature. And JAX, which I was originally biased against since it's a Google product. But some close friends persuaded me to try it, and I actually liked it. However, I thought that it would be difficult for JAX to gain widespread adoption in the community, as PyTorch already had a strong network effect and was gaining traction quickly. I didn't see how anyone could catch up with PyTorch. Another issue with JAX is that it requires additional cognitive load for developers. Take a look: https://higgsfield.substack.com/p/how-to-design-api-of-machine-learning submitted by /u/Good-Willingness-985 [link] [comments]  ( 9 min )

Github : https://github.com/OutofAi/2D-Gaussian-Splatting https://i.redd.it/cwgsjtko1sub1.gif submitted by /u/TerryCrewsHasacrew [link] [comments]  ( 8 min )

Data products plays an important role in building state of the art machine learning models. Though their building process seems a bit confusing within industry as of now, this article series tries to simplify it by breaking it and explaining it into 4 steps. Take a look: https://moderndata101.substack.com/p/how-to-build-data-products-deploy What processes are being followed at your org for building scalable data products? submitted by /u/growth_man [link] [comments]  ( 9 min )

Hey Guys, It seems RAG is really taking off as an increasingly popular use case for LLMs to leverage contextual data. However, everybody is building their own contextual data sets and embedding them in their own silo'd vector dbs. Do you guys think there's any utility in having a shared public vector db that anyone can tap into their API, without having to self-host, worry about the embedding pipelines and filling the vector db with enough data in the first place for their use cases? Would this save devs alot of time in quickly testing testing product ideas? (albeit it does seem that propriety data is what everyone's raving about today) - For context, I'm building a social media product we're users can upload a few pieces (approx 10) of content (social media posts, websites, videos to start with), which becomes the verified human-curated list/Niche. We then classify and embed this into a vector db. From this, we have set up a data pipeline to scrape the web and find new content that is most similar which we suggest to users to add to the Niche (upvote, downvote style). When a piece of content is upvoted on its added to the verified list updating the Niche's classification string. Essentially we're aiming to construct an ever-growing, user-curated, contextually classified vector database from a relatively small set of sample data. submitted by /u/niksteel123 [link] [comments]  ( 9 min )

Hi. I'm curious if there have been any studies done regarding the effects of using data generated by LLMs for other downstream tasks. The closest that I could find are the two papers: Large Language Model as Attributed Training Data Generator: A Tale of Diversity and Bias (Yu et al., 2023) Generating Training Data with Language Models: Towards Zero-Shot Language Understanding (Meng et al., 2022) The former focuses on studying the differences between the type of prompts that are used to generate the data and the latter doesn't use LLMs. Doesn't have to be papers, blog posts or any sort of information regarding the scenario I described is fine. Thanks. submitted by /u/Seankala [link] [comments]  ( 9 min )

Hello, I am working on an app that requires creating lots of text embeddings(100M tokens). Looking at OpenAI Ada pricing(and considering that my app doesn't yet make any money) I'm looking into self-hosting a model to run on CPU. I know that constrains me towards smaller models-- so far locally I've been testing with sentence-transformers/all-MiniLM-L6-v2 and the query results seem okay-ish enough for my MVP. (Although, I should not that I haven't compared how embeddings with other models would perform.) Does anyone have experiences doing something similar? In particular, I'd love to hear about any tips you have for maximizing no. of embeddings / second. (new to ML/MLOps, so apologies if this is a silly question :) submitted by /u/rsamrat [link] [comments]  ( 9 min )

What is this? stable-fast is an ultra lightweight inference optimization library for HuggingFace Diffusers on NVIDIA GPUs. stable-fast provides super fast inference optimization by utilizing some key techniques and features: CUDNN Convolution Fusion: stable-fast implements a series of fully-functional and fully-compatible CUDNN convolution fusion operators for all kinds of combinations of Conv + Bias + Add + Act computation patterns. Low Precision & Fused GEMM: stable-fast implements a series of fused GEMM operators that compute with fp16 precision, which is fast than PyTorch's defaults (read & write with fp16 while compute with fp32). NHWC & Fused GroupNorm: stable-fast implements a highly optimized fused NHWC GroupNorm + GELU operator with OpenAI's triton, which eliminates the need…  ( 9 min )

Hello, I was looking at the Flan T5 paper and code. It was clear that the question (instruction) and the context are given to the encoder as input. But I find no details on what does the decoder take as input apart from the fact that it starts with the pad token. Anyone can give me more details please ? Thanks ! submitted by /u/Meddhouib10 [link] [comments]  ( 9 min )

I am curious about the state of the ecosystem for JavaScript, where TF looks like a reasonably solid option. Options I have found so far are: TensorFlow.js (looks like the most complete solution, but the general sentiment about TF in Python is pretty bad!) MediaPipe (to quickly implement specific use cases it seems, maybe using tf.js in the background?) ml5.js (a layer on top of tf.js to make it more approachable if i understand correctly) transformers.js (haven't quite grasped this one) shumai (bun only, so server side only) I am curious to read informed opinions about these and more! Have you used them and how? ​ submitted by /u/gtnbssn [link] [comments]  ( 9 min )

Hi all. I'm preparing an open website about products used for AI/ML/DL computation (no in-house testing for now, just the database and GUI). However comparing raw speed of products of different vendors is more challenging than I anticipated, because there are many possible raw performance indicators, only few of which are provided by vendors. For example a raw performance indicator can be "FP32 vector with opportunistic optimization", while another can be "BF16 matrix/tensor without opportunistic optimization". A full picture of raw performance would be fully represented only by a table with multiple dimensions: Number format (FP64, FP32, TF32, FP16, BF16, FP8, INT8, INT4... are the others?) Vector vs. matrix/tensor operation (boolean) Opportunistic optimizations like Nvidia Sparsity…  ( 9 min )

Wondering if anyone has some interesting loss graphs that they could share. Maybe loss suddenly dropped after 100 epochs, or a local minima was found and then it jumped into a lower one. Wondering if anyone forgot to turn off training and cam back to an improved result than what they thought had already been converged to. submitted by /u/HStuart18 [link] [comments]

I've had access to Dall-3, Vision and voice chat features, and I've been blown away by how impressive each of the new features are. Dall-E 3 seems roughly comparable to Midjourney in overall image quality, but does a much better job at understanding the prompt. The vision model continues to surprise by how well it is able to understand images at a seemingly human level of comprehension. And the voice chat is such an intuitive and captivating way of interacting with ChatGPT, it felt like I was interacting with one of the AI assistants from the movie "Her". However, it's unfortunate that these amazing new features cannot be used together at the same time. Up until gaining access to these features, I had been using the advanced data analysis model as my default, which is great for helping with programming tasks. I can only imagine how revolutionary ChatGPT will be when a cohesive multi-modal model is released sometime in the near future which has all these capabilities available from the start. What things would you want to try if such a cohesive model was released? I can already imagine some use cases where you could set up iterative improvement for things like interface design, which some people have already got to work with just the base vision model by itself. submitted by /u/ImRealNow [link] [comments]

The Biden administration has announced additional limits on sales of advanced semiconductors by American firms to China, in an effort to restrict China's progress on supercomputing and artificial intelligence. The new rules will likely halt most shipments of advanced semiconductors from the United States to Chinese data centers, which use them to produce models capable of artificial intelligence. Chip makers seeking to sell China advanced chips or the machinery used to make them will be required to notify the government of their plans or obtain a special license. To prevent the risk of advanced U.S. chips reaching China through third countries, chip makers will also need licenses to ship to other countries subject to U.S. arms embargoes. The Biden administration argues that China's access to advanced technology is dangerous as it could aid the country's military in tasks like guiding hypersonic missiles or cracking top-secret U.S. codes. The restrictions may affect Chinese companies developing AI chatbots and could weaken China's economy in the long run, as AI is transforming industries from retail to healthcare. The limits are also expected to impact sales to China of U.S. chip makers such as Nvidia, AMD, and Intel, who earn a significant portion of their revenue from Chinese buyers. The rules will exempt chips used in commercial applications like smartphones, laptops, electric vehicles, and gaming systems. The Semiconductor Industry Association, which represents major chip makers, is evaluating the impact of the updated rules. The Biden administration has been trying to counter China's growing mastery of cutting-edge technologies by investing in new chip factories in the U.S. while setting restrictions on exports of technology to China. Source : https://www.nytimes.com/2023/10/17/business/economy/ai-chips-china-restrictions.html submitted by /u/NuseAI [link] [comments]

Google has asked a California federal court to dismiss a proposed class action lawsuit that claims the company's scraping of data to train generative artificial-intelligence systems violates millions of people's privacy and property rights. Google argues that the use of public data is necessary to train systems like its chatbot Bard and that the lawsuit would 'take a sledgehammer not just to Google's services but to the very idea of generative AI.' The lawsuit is one of several recent complaints over tech companies' alleged misuse of content without permission for AI training. Google general counsel Halimah DeLaine Prado said in a statement that the lawsuit was 'baseless' and that U.S. law 'supports using public information to create new beneficial uses.' Google also said its alleged use of J.L.'s book was protected by the fair use doctrine of copyright law. Source : https://www.reuters.com/legal/litigation/google-says-data-scraping-lawsuit-would-take-sledgehammer-generative-ai-2023-10-17/ submitted by /u/NuseAI [link] [comments]

It regressed to mean... PS: I read the sidebar which didn't exclude humor, and the flair seems to suggest that it would be okay, but my apologies if not. submitted by /u/Tyler_Zoro [link] [comments]

submitted by /u/trcytony [link] [comments]

Researchers evaluated ChatGPT and GPT-4 on mock CFA exam questions to see if they could pass the real tests. The CFA exams rigorously test practical finance knowledge and are known for being quite difficult. They tested the models in zero-shot, few-shot, and chain-of-thought prompting settings on mock Level I and Level II exams. The key findings: GPT-4 consistently beat ChatGPT, but both models struggled way more on the more advanced Level II questions. Few-shot prompting helped ChatGPT slightly Chain-of-thought prompting exposed knowledge gaps rather than helping much. Based on estimated passing scores, only GPT-4 with few-shot prompting could potentially pass the exams. The models definitely aren't ready to become charterholders yet. Their difficulties with tricky questions and core finance concepts highlight the need for more specialized training and knowledge. But GPT-4 did better overall, and few-shot prompting shows their ability to improve. So with targeted practice on finance formulas and reasoning, we could maybe see step-wise improvements. TLDR: Tested on mock CFA exams, ChatGPT and GPT-4 struggle with the complex finance concepts and fail. With few-shot prompting, GPT-4 performance reaches the boundary between passing and failing but doesn't clearly pass. Full summary here. Paper is here. submitted by /u/Successful-Western27 [link] [comments]

GPT4 vision isn't just a gimmick. We've been given a new superpower, and so we must "deal with it". This is probably as big a moment as when chatGPT first arrived, maybe more. Machine Vision for the masses (and more). I tried doing some very loose sketches, and it really struggled to identify them until they were coloured in. Humans could easily what they were. But, in order to see what uses it has, we need to know what capabilities it does and does not have. Pick a question and see what you can learn! can it use TINY images (I assume they are much faster) can it tell you what has changed in two images? can it measure distances ? (with perspective?) can it make 3d models from instructions? can it "learn" to recognise people/ similar objects (in the same context window) what limits are there to exhaustive listing exhaustive description is it better at details or overviews can it read maps / graphs / text how smart is it on DIY / xrays / mechanics can it follow wires?? (Can it find lego) is there a formal reference system you can use (X/Y) can it give co-ordinates in large grids or grid-like (how un-grid like) ie film strip, or window-panes can it navigate a 2d maze turn-by turn? 3d maze? can that be insanely complex? can it make ebay descriptions (condition) can it estimate food weight can it estimate strength / angles / volume can it create programs from screenshots. Can it use programs? games? control RC car / robot? what kind of language / instructions are best when talking about images. what other questions do we need submitted by /u/inteblio [link] [comments]

Yann LeCun and Yoshua Bengio, two influential figures in AI and deep learning, engaged in a heated debate over the potential risks and safety concerns surrounding AI. LeCun emphasized the need to design AI systems for safety rather than imagining catastrophic scenarios. Bengio argued for the importance of prudence, stating that we still do not understand how to design safe, powerful AI systems, and highlighted the need for major investment in AI safety and governance. The debate highlighted the disagreement among esteemed researchers about AI's potential risks, the effectiveness of current safety measures, and the best path forward. The implications of AI, including job displacement, privacy violations, and existential risks, have become a topic of widespread concern. Source : https://venturebeat.com/ai/ai-pioneers-yann-lecun-and-yoshua-bengio-clash-in-an-intense-online-debate-over-ai-safety-and-governance/ submitted by /u/NuseAI [link] [comments]

Amazon Pharmacy is a full-service pharmacy on Amazon.com that offers transparent pricing, clinical and customer support, and free delivery right to your door. Customer care agents play a crucial role in quickly and accurately retrieving information related to pharmacy information, including prescription clarifications and transfer status, order and dispensing details, and patient profile information, in […]  ( 8 min )

At Amazon Web Services (AWS), not only are we passionate about providing customers with a variety of comprehensive technical solutions, but we’re also keen on deeply understanding our customers’ business processes. We adopt a third-party perspective and objective judgment to help customers sort out their value propositions, collect pain points, propose appropriate solutions, and create […]  ( 16 min )

Amazon Personalize has launched a new integration with Amazon OpenSearch Service that enables you to personalize search results for each user and assists in predicting their search needs. The Amazon Personalize Search Ranking plugin within OpenSearch Service allows you to improve the end-user engagement and conversion from your website and app search by taking advantage […]  ( 7 min )

Announcements Top Stories In-Depth The post DSC Weekly 17 October 2023 appeared first on Data Science Central.  ( 20 min )

In a recent podcast episode, Lex Freedman and Mark Zuckerberg convened in the Metaverse, where the digital realm intertwines with reality. Their astonishingly realistic interaction, while highlighting technological advancements, also prompted deeper contemplations. As the line between digital recreations and reality becomes increasingly blurred, it beckons questions about the definitions of identity and consciousness and… Read More »Uncharted digital landscapes and the quest for timeless identity The post Uncharted digital landscapes and the quest for timeless identity appeared first on Data Science Central.  ( 22 min )

Introduction to Internet of Things (IOT): Internet of Things (IoT) represents the fourth-generation technology that facilitates the connection and transformation of products into smart, intelligent and communicative entities. IoT has already established its footprint in various business verticals such as medical, heath care, automobile, and industrial applications. IoT empowers the collection, analysis, and transmission of… Read More »Internet Of Things (IOT):  Application In Hazardous Locations The post Internet Of Things (IOT):  Application In Hazardous Locations appeared first on Data Science Central.  ( 23 min )

Long before passengers sit back, relax, and enjoy their flight, data has played a critical role in getting them to their seats. It has been a cornerstone of the aviation industry since the early days of air travel. Indeed, from the early 20th century, data was collected through manual processes such as pilots logging information… Read More »The digital evolution in aviation: how big data and analytics are transforming the industry The post The digital evolution in aviation: how big data and analytics are transforming the industry appeared first on Data Science Central.  ( 20 min )

submitted by /u/gwern [link] [comments]

submitted by /u/gwern [link] [comments]

Hello, I’m working on a scenario that involves many systems and each system involves many subsystems. At each decision time and according to the system that requests the decision, the RL agent must select a subsystem. Nevertheless, each system has a different number of subsystems which makes the action space and the state space dynamic since the each neurone in the output represents a subsystem. Can I use the maximal number of subsystems (not the total number) as the number of the output and masking some neurones according to the current system ? submitted by /u/GuavaAgreeable208 [link] [comments]

I am new to RL and have a naive question. How interpretable would the policy be from building a rl algorithm in an offline setting? Could I make inferences about what the optimal sequences would be? submitted by /u/kwsunshine123 [link] [comments]

Goal Representations for Instruction Following Figure title. Figure caption. This image is centered and set to 50% page width. --> A longstanding goal of the field of robot learning has been to create generalist agents that can perform tasks for humans. Natural language has the potential to be an easy-to-use interface for humans to specify arbitrary tasks, but it is difficult to train robots to follow language instructions. Approaches like language-conditioned behavioral cloning (LCBC) train policies to directly imitate expert actions conditioned on language, but require humans to annotate all training trajectories and generalize poorly across scenes and behaviors. Meanwhile, recent goal-conditioned approaches perform much better at general manipulation tasks, but do not enable easy t…  ( 7 min )

GeForce RTX and NVIDIA RTX GPUs, which are packed with dedicated AI processors called Tensor Cores, are bringing the power of generative AI natively to more than 100 million Windows PCs and workstations.  ( 7 min )

NVIDIA today announced an update to RTX Video Super Resolution (VSR) that delivers greater overall graphical fidelity with preserved details, upscaling for native videos and support for GeForce RTX 20 Series GPUs.  ( 7 min )

Researchers coaxed a family of generative AI models to work together to solve multistep robot manipulation problems.  ( 11 min )

submitted by /u/Chipdoc [link] [comments]  ( 8 min )

submitted by /u/Stanford_Online [link] [comments]

🌟 Excited to share our recent research, DexCatch! Pick-and-place is slow and boring, while throw-catching is a behaviour towards more human-like manipulation. We propose a new model-free framework that can catch diverse objects of daily life with dexterous hands in the air. This ability to catch anything from a cup to a banana, and a pen, can help the hand quickly manipulate objects without transporting objects to their destination -- and even generalize to unseen objects. Video demonstrations of learned behaviors and the code can be found at https://dexcatch.github.io/. ​ https://reddit.com/link/17973ri/video/i4xdo39d4lub1/player submitted by /u/Shengjie_Wang [link] [comments]

I am reading this paper and came across the following paragraph - ​ "Model usage. Many recent model-based algorithms have focused on the setting in which model rollouts begin from the initial state distribution (Kurutach et al., 2018; Clavera et al., 2018). While this may be a more faithful interpretation of Algorithm 1, as it is optimizing a policy purely under the state distribution of the model, this approach entangles the model rollout length with the task horizon. Because compounding model errors make extended rollouts difficult, these works evaluate on truncated versions of benchmarks. The branching strategy described in Section 4.2, in which model rollouts begin from the state distribution of a different policy under the true environment dynamics, effectively relieves this limitation. In practice, branching replaces few long rollouts from the initial state distribution with many short rollouts starting from replay buffer states." ​ What does state distribtion mean over here? Also in line 8 of the image, I don't understand what's the relation between model rollout and policy \pi_t. Is it saying, use the model free algorithm to take future steps from that state? What does the model have to do with that? ​ https://preview.redd.it/twlej5my3kub1.png?width=1182&format=png&auto=webp&s=4a515c8d237c963052bc1b60a9e7dda53a33f001 submitted by /u/Academic-Rent7800 [link] [comments]

hi all! i’m an undergraduate that is really interested in pursuing a PhD. i think reinforcement learning is especially interesting, causal reinforcement learning in particular. for my current research job, which unfortunately doesn’t really involve ML, i read a little about causal inference and it really intrigued me. what mathematics courses should i take to get into RL research at a theoretical/algorithmic level? i am currently taking proof-based linear algebra, and have taken all the computational calculus offered. i imagine prob. theory/math stats is pretty important, too; what else? submitted by /u/treeman0469 [link] [comments]

Hello everyone, I'm currently working on Retrieval Augmented Generation (RAG) models and have developed a custom chunking function, as I found the methods in LangChain not entirely satisfactory. I'm keen on exploring other methods, algorithms (related to NLP or otherwise), and models to enhance text chunking in RAG. There are many RAG implementations out there, but I've noticed a lack of focus on improving chunking performance specifically. Are there any other promising approaches beyond my current pipeline, which consists of a bi-encoder (retriever), cross-encoder (reranker), and a Large Language Model (LLM) for interactions? For queries, I'm using both traditional and HyDE (Hypothetical Document Embedding) approaches in the retrieval phase, and sending the top 'n' results of both similarity search to the reranker. I've also tried using an LLM to convert the query into a series of 10-20 small phrases or keywords, which are then used as the query for the retriever model. However, the results vary depending on the LLM used. To generate good keywords (with a not extractive approach) , I had to use a "CoT" prompt, instructing the model to write self-instruct, problem analysis and reasonings before generating the required keywords. But this approach use lots of tokens, and requires careful scraping to ensure the model has used the right delimiter to separate reasoning and the actual answer. I'm also planning to modify the text used to generate embeddings, while returning the original text after the recall phase. But this is still a work in progress and scaling it is proving to be a challenge. If anyone has any tips or experience with this, I'd appreciate your input. I'd be grateful for any resources, repositories, libraries, or existing implementations of novel chunking methods that you could share. Or we could just discuss ideas, thoughts, or approaches to improve text chunking for RAG here. Thanks in advance for your time! submitted by /u/BXresearch [link] [comments]  ( 9 min )

I started learning deep learning last year and decided to step up my game with regard to model training and tools. I recently built a GPU server. It’s still within its return period, so please help decide if it’s worth keeping: Processor: 2x Xeon E5-2690 v4 2.6GHz 14-Core Memory: 128GB GPU: 8x NVIDIA Tesla P100 16GB HBM2 Accelerator Card Total cost: ~$3200 submitted by /u/Stonks-Stocks [link] [comments]  ( 9 min )

We are hosting a few "How to Apply to Grad School" webinars this week. This is a chance to hear from faculty and students in the Robotics Institute at CMU on what life in grad school is actually like, as well as get some tips on crafting a strong application! https://cmu-ri-resources.github.io/ submitted by /u/bart-ai [link] [comments]  ( 9 min )

Tables pack tons of relational data but are tough for AI to grasp. They have complex 2D structure with information scattered across rows and columns. Models like GPT-3 fail basic tasks like finding where a missing value should go. LLMs struggle at this because they're pre-trained mostly on natural text, which is linear. Researchers at Microsoft wanted to mitigate this with "table-tuned" models, trained on table-related tasks. Their process: Automatically generate lots of diverse table-task training cases from a corpus of real-world tables. Ex: "impute missing value" or "identify error in table". Further augment data via paraphrasing, shuffling table rows/columns, chaining model responses, etc. This table-tuning produced "Table-GPT" models with substantially stronger table skills. In experiments, Table-GPT crushed vanilla GPT-3: 25%+ better on unseen table tasks like missing value ID and column type ID Beat GPT-3 on 98% of test cases across 9 different table tasks Stayed superior after downstream tuning too There's tons more work to do but seems pretty promising. Table-tuning boosted models' ability to comprehend tables and reason over tabular data vs just pre-training on text. TLDR: Training AI models more on synthesized table tasks ("table-tuning") significantly improves their table skills. Full summary is here. Paper is here. submitted by /u/Successful-Western27 [link] [comments]  ( 9 min )

When are we getting a text-to-pose ai? I'd love to be able to generate poses for 3d models that match a given text description, because sometimes what my mind comes up with doesn't feel adequate. It's frustrating that I'm not seeing any developments in this area of ai, and I lack the skills to commence the developments myself. submitted by /u/BM09 [link] [comments]  ( 9 min )

submitted by /u/currentscurrents [link] [comments]  ( 8 min )

I am ok with paying for the data. I just can't find any sources for it. I found some data on github that appears to come from container ships at port, but nothing for a ship underway. submitted by /u/jschall2 [link] [comments]  ( 9 min )

Hi, I have a dataset with video and other modalities (e.g. audio), and I want to run a captioning task. I found UniVL, which is a pre-trained model that supports video and text (transcripts) and can caption them. It extracts features and runs transformer encoders on both these modalities to get an embedding, then concatenates them and feeds it into a cross-encoder and decoder to get captions. I'm wondering if I can make use of this model, but add in other modalities, by writing my own embedding model and feeding the embeddings into the cross encoder. Would this work? Is there any similar previous work regarding adding new modalities to a pre-trained network? submitted by /u/joeswansonx69x [link] [comments]  ( 9 min )

I've seen classic papers before 2021 that have been quite influential - RL and evolution based strategies. I have also seen: differentiable approaches: https://arxiv.org/abs/1806.09055 zero-learning approaches: https://arxiv.org/abs/2006.04647 But these are all papers pre-2021. From people who are familiar with this field, what is the current SOTA of neural architecture search (NAS) post 2022? i.e. papers that can serve as the most relevant baselines? Thank you! :) ​ ​ ​ submitted by /u/Cultural-Average3959 [link] [comments]  ( 9 min )

Is active learning a dying topic when zero shot learning came out? Active learning is to used few labeled samples plus a initially trained model to select the most useful unlabeled data for training. Zero/few shot learning is to train a model on some data then Mae it work directly with unseen label/data. In my understanding, zero/few short learning is more aligned with the current large model trend or foundation model trend. Active learning strategy seems to still rely on small dataset and was intending to gradually enrich training data by selecting new samples in. In industry and in big tech, which one is more used or deployed? Anyone can give me some comments? submitted by /u/Little-Bumblebee-452 [link] [comments]  ( 9 min )

Hi all, Building off our last research post, we wanted to figure out ways to quantify "ambiguity" and "uncertainty" in prompts/responses to LLMs. We ended up discovering two useful forms of uncertainty: "Structural" and "Conceptual" uncertainty. In a nutshell: Conceptual uncertainty is when the model isn't sure what to say, and Structural uncertainty is when the model isn't sure how to say it. You can play around with this yourself in the demo or read about it in more detail in the blog post submitted by /u/shayanjm [link] [comments]  ( 9 min )

I often hear from folks with very large datasets saying: “my labelling costs keep increasing, but we don’t see model performance improvements” or “my storage and compute costs are rising (for a dataset of 1M+ images) but performance just stalled”. This post argues that large datasets have hidden costs, beyond time and money, poor data quality and the wrong selection process might be killing model performance. Any thoughts? Have you faced this challenge? submitted by /u/btcmx [link] [comments]  ( 9 min )

Hello, everyone! I'm seeking tips to enhance my semantic search pipeline. Currently, I'm working on a semantic search tool. Given a set of text files, my goal is to retrieve the most relevant information related to the query. To achieve this, I begin by preprocessing the PDF files, splitting them into pages, and computing embeddings using a fine-tuned BERT model for Italian. Next, with a query and its embedding, I calculate the cosine similarity to all the pages in the document. Since there aren't many pages, a brute search remains quite fast. However, I'm encountering an issue where the similarity results don't consistently yield the most relevant information. I've experimented with various embedding layers, but there's been little to no improvement. I've also tested a commercially available solution to ensure the problem isn't with my PDF files. Interestingly, I achieved better results, leading me to believe that the issue may lie within my pipeline. My current hypothesis is that the page splitting process might be excluding relevant semantic connections, and I may need to improve my text preprocessing. What suggestions do you have to enhance my results? P.S. The information obtained from the similarity check is subsequently used as context with a chat language model, similar to tools like AsMyPdf. submitted by /u/AcquaFisc [link] [comments]  ( 9 min )

I have a couple of terabytes of checkpoints, and I desperately need to free up some space, without deleting those atm. Is there a compression algorithm that can handle such data successfully? I tried gzip with tar but the compressed size ended up being only ~100G less - that's when I realized that (gzip) compression algo is not good at handling seemingly random numerical data. Do you know of methods that've proven to work in this scenario? submitted by /u/OpeningVariable [link] [comments]  ( 9 min )

https://arxiv.org/pdf/2310.02226.pdf Abstract Language models generate responses by producing a series of tokens in immediate succession: the (K+1)th token is an outcome of manipulating K hidden vectors per layer, one vector per preceding token. What if instead we were to let the model manipulate say, K+10 hidden vectors, before it outputs the (K+1)th token? We operationalize this idea by performing training and inference on language models with a (learnable) pause token, a sequence of which is appended to the input prefix. We then delay extracting the model's outputs until the last pause token is seen, thereby allowing the model to process extra computation before committing to an answer. We empirically evaluate pause-training on decoder-only models of 1B and 130M parameters with causal pretraining on C4, and on downstream tasks covering reasoning, question-answering, general understanding and fact recall. Our main finding is that inference-time delays show gains when the model is both pre-trained and finetuned with delays. For the 1B model, we witness gains on 8 of 9 tasks, most prominently, a gain of 18% EM score on the QA task of SQuAD, 8% on CommonSenseQA and 1% accuracy on the reasoning task of GSM8k. Our work raises a range of conceptual and practical future research questions on making delayed next-token prediction a widely applicable new paradigm. Here is a Medium post about my thoughts on the paper. submitted by /u/transformer_ML [link] [comments]  ( 9 min )

DPO Paper I read a really fascinating paper where RL was used on LLMs to make them better at interacting in embodied environments. https://arxiv.org/abs/2310.08588 The technique was called Reinforcement Learning with Environmental Feedback (RLEF). In the paper PPO was used, but I'm wondering if DPO could be used to replace it? submitted by /u/30299578815310 [link] [comments]  ( 9 min )

i built my own custom generative ai chatbot model. only thing i need is high quality and diverse dataset to train my model. i cant use already existing datasets because i dont think they are diverse and quality enough.so i need to create it using gpt4. my dataset will have 3 columns ; system_prompt, input, output. but im not very experienced on creating datasets, and i couldnt find any resources about this. all input ,output and system prompt all should be created by gpt4. how can i do it? and what is most effective way to use api for this? submitted by /u/Many-Corner-6700 [link] [comments]  ( 9 min )

Merge conflicts are something that give developers hours of headaches and I figured I would try and give my take on a solution. I followed a paper from IEEE engineers in 2022 who trained CodeBert on merge conflicts as a classification task, and they published their dataset for public use. Input formatted as “>>>>>>” will output the attempted conflict resolution. I am still trying to find out how to do evaluations on this model as the loss applies to all sections not just the resolution, and the TRL Trainer with a data collator gives NaN as a loss. The model and dataset are on HuggingFace under codys12/MergeLlama and codys12/MergeLlama-7b. Any feedback is appreciated! submitted by /u/cstein123 [link] [comments]  ( 9 min )

Hey, I've built a set of extensions for OpenTelemetry that provides visibility into LLM applications like RAG pipelines - whether it be prompts, vector DBs and more. Here’s the repo: https://github.com/traceloop/openllmetry. Two key benefits with OpenTelemetry are - You can trace your entire system execution, not just the LLM (so you can see how requests to DBs, or other calls affect the overall result); You can connect to any monitoring platform—no need to adopt new tools. Install the SDK and plug it into Datadog, Sentry, or both. Or switch between them easily. There's already support for OpenAI, Anthropic, Cohere, Pinecone, Chroma, LangChain, and Haystack and we are working hard to support the entire ecosystem. Would love to hear your thoughts submitted by /u/nirga [link] [comments]  ( 9 min )

Exploiting AI to make software programming easier? SWE-bench, a unique evaluation system, tests language models' ability to solve real GitHub-collated programming issues. Interestingly, even top-notch models manage only the simplest problems, underscoring tech development's urgency for providing practical software engineering solutions. For the latest advancements in AI, look here first. https://preview.redd.it/rq5vl22bckub1.png?width=1292&format=png&auto=webp&s=d79988bfe0ab37b0f97f55296d7a7341c9292c11 A New Approach to Evaluating AI Models Researchers use real-world software engineering problems from GitHub to assess language models' coding problem-solving skills. SWE-bench, introduced by Princeton and the University of Chicago, offers a more comprehensive and challenging benchmark…  ( 9 min )

I am just puzzled how does one efficiently query a huge transformer model such that so many users can be served at the same time. Is it queried on per user basis? (modulo some caching) If yes, how expensive is this? If no, what the hell is going on? :D Are there any good resources on this? (how to build large scale apps with big models, from scratch). Somehow this doesn't really fit the standard data-intensive system design process, or maybe I am missing something. submitted by /u/jimmymvp [link] [comments]  ( 9 min )

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I always did want to draw a comic but I was never very good at drawing even though I put a lot of effort into it when I was younger... :'( So when I stumbled on image generation AI, I thought maybe it could help me transform my doodles into something decent. It took me a while and a lot of effort to write a tool to help me with that : story and dialogues are my own, images are based on doodles enhanced by AI. I would love to have feedback about the story : https://stripik.com/story/4/chapter/4/ ​ https://preview.redd.it/dvcudd4j3mub1.png?width=800&format=png&auto=webp&s=717bef60eaaf9b9a35a1a66f266c374406a923fa submitted by /u/maxcmoi [link] [comments]

submitted by /u/SexyJimBelushi [link] [comments]

Did you play these (SEO) games? 👾 https://preview.redd.it/yxuzllzupkub1.jpg?width=661&format=pjpg&auto=webp&s=23ebc6e972ac85b152aa8b69f48e2b0c5bae2c76 https://preview.redd.it/x8zfokzupkub1.jpg?width=661&format=pjpg&auto=webp&s=be2163a7bfbeee64a63c1292a5b4c482c5be33ae https://preview.redd.it/eerpgnzupkub1.jpg?width=661&format=pjpg&auto=webp&s=d8eceafd3732653c743a6731ae5932c9e0da071c https://preview.redd.it/uxwgskzupkub1.jpg?width=661&format=pjpg&auto=webp&s=07c751eaa16f8fa484034c98a3c1fd0b2162f5a2 Source: https://twitter.com/carlos_darko/status/1713900305765605484 submitted by /u/DanielPeris [link] [comments]

Exploiting AI to make software programming easier? SWE-bench, a unique evaluation system, tests language models' ability to solve real GitHub-collated programming issues. Interestingly, even top-notch models manage only the simplest problems, underscoring tech development's urgency for providing practical software engineering solutions. For the latest advancements in AI, look here first. https://preview.redd.it/8laeg7cbckub1.png?width=1292&format=png&auto=webp&s=e549f0045a7253cd2d3f351d8297a301c4cbf6ac A New Approach to Evaluating AI Models Researchers use real-world software engineering problems from GitHub to assess language models' coding problem-solving skills. SWE-bench, introduced by Princeton and the University of Chicago, offers a more comprehensive and challenging benchmark…

What is the best free tool to make a video where the language changes? submitted by /u/Easy_Technology6768 [link] [comments]

New York-based tech firms and investors see the advent of AI as the latest opportunity to try to unseat the Bay Area as tech’s global capital.[1] Microsoft announced a new “bug bounty” program, vowing to reward security researchers between $2,000 and $15,000 if they’re able to find “vulnerabilities” in its Bing AI products, including “jailbreak” prompts that make it produce responses that go against the guardrails that are supposed to bar it from being bigoted or otherwise problematic.[2] OpenAI is preparing to launch a suite of updates to make it more cost-effective and efficient for developers to create software applications with AI models.[3] TCS Seeks to Use Microsoft AI Partnership to Improve Margins.[4] Sources: [1] https://www.axios.com/2023/10/12/new-york-ai-world-capital [2] https://futurism.com/the-byte/microsoft-bing-ai-bug-bounty [3] https://www.techedt.com/openai-aims-to-attract-developers-with-cost-effective-updates-insiders-reveal [4] https://www.bloomberg.com/news/articles/2023-10-15/tcs-seeks-to-use-microsoft-ai-partnership-to-improve-margins#xj4y7vzkg submitted by /u/Excellent-Target-847 [link] [comments]

I was wondering if any AI image generation was good at this (yet?). I have a real-life image I want to upload and get AI to generate what that would most likely look like from the vantage point of someone standing at a different angle. submitted by /u/YepperyYepstein [link] [comments]

Hi there, anybody knows how AI dubbing translator works ? As im interested if something similiar to https://app.rask.ai/ exist localy ?? Is there anything from github? Im looking for czech language. I know you can scribe audio to text than translate text and let AI to talk this text. But is there a tool that do all of this in one click ? Thank you and have a nice day. submitted by /u/Low_Government_681 [link] [comments]

Some researchers see formal specifications as a way for autonomous systems to "explain themselves" to humans. But a new study finds that we aren't understanding.  ( 9 min )

Veriff is an identity verification platform partner for innovative growth-driven organizations, including pioneers in financial services, FinTech, crypto, gaming, mobility, and online marketplaces. In this post, we show you how Veriff standardized their model deployment workflow using Amazon SageMaker, reducing costs and development time.  ( 8 min )

How trustworthy are generative pre-trained transformer (GPT) models? To answer this question, University of Illinois Urbana-Champaign, together with Stanford University, University of California, Berkeley, Center for AI Safety, and Microsoft Research, released a comprehensive trustworthiness evaluation platform for large language models (LLMs), which is presented in the recent paper: DecodingTrust: A Comprehensive Assessment of Trustworthiness […] The post DecodingTrust: A Comprehensive Assessment of Trustworthiness in GPT Models appeared first on Microsoft Research.  ( 11 min )

Introduction Hello AI&ML Engineers, as you all know, Artificial Intelligence (AI) and Machine Learning Engineering are the fastest growing fields, and almost all industries are adopting them to enhance and expedite their business decisions and needs; for the same, they are working on various aspects and preparing the data for the AIML platform with the help of SMEs… Read More »Explainable Artificial Intelligence (XAI) for AI & ML Engineers The post Explainable Artificial Intelligence (XAI) for AI & ML Engineers appeared first on Data Science Central.  ( 23 min )

From Love What Lasts, Joshua Gibbs: … there are too many things in the world to care equally about them all. The sheer volume of things … demands that we have hierarchical standards by which to judge their value, or else we are condemned to give our lives over entirely to what is nearest, easiest, […] Nearest, easiest, and most accessible first appeared on John D. Cook.  ( 4 min )

Similar to my article series on adversarial robustness, I was planning to have a series on bit errors robustness accompanied by PyTorch code. Instead, due to time constraints, I decided to condense the information into a single article. The code for the originally planned six articles is available on GitHub. The post Benchmarking Bit Errors in Quantized Neural Networks with PyTorch appeared first on David Stutz.  ( 6 min )

Rethinking the Role of PPO in RLHF TL;DR: In RLHF, there’s tension between the reward learning phase, which uses human preference in the form of comparisons, and the RL fine-tuning phase, which optimizes a single, non-comparative reward. What if we performed RL in a comparative way? Figure 1: This diagram illustrates the difference between reinforcement learning from absolute feedback and relative feedback. By incorporating a new component - pairwise policy gradient, we can unify the reward modeling stage and RL stage, enabling direct updates based on pairwise responses. Large Language Models (LLMs) have powered increasingly capable virtual assistants, such as GPT-4, Claude-2, Bard and Bing Chat. These systems can respond to complex user queries, write code, and even produce poetry. T…  ( 6 min )

Draw three circles of radius r that intersect at a single point. Then draw a triangle connecting the remaining three points of intersection. (Each pair of circles intersects in two points, one of which is the point where all three circles intersect, so there are three other intersection points.) Then the circumcircle of the triangle, […] Johnson circle theorem first appeared on John D. Cook.  ( 5 min )

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Game dev is super complex nowadays - games have huge codebases, massive teams, and dev cycles dragging on for years. Costs are insane too - budgets can hit $100M+ easily. In a new paper, researchers propose to reverse this trend with an AI framework called GameGPT that automates parts of the dev process using multiple AI agents. Each agent handles a different role (all are fine-tuned from relevant base models): One agent reviews the game design plan to catch errors Another turns tasks into code implementations Reviewer agents check the code and results A testing agent validates everything works as expected By breaking up the workflow, GameGPT can simplify things for the AI agents. They just focus on a narrow role versus having one jack-of-all-trades agent. The authors argue GameGPT can eliminate repetitive and rote elements of gamedev like testing. This would free up developers to focus on creative design challenges. However, the GameGPT paper does not include any concrete results or experiments demonstrating improved performance. There is no evidence presented that GameGPT reduces hallucinations, redundancy or development time. The authors mention empirical results support their claims that the architecture is more effective, but none are provided. I could not find any additional support material about this work, like a project website, that I could use to further check into this (maybe someone can share in the comments?). Right now GameGPT seems mostly conceptual. The ideas are interesting but hard to assess without quantitative results. TLDR: New GameGPT AI framework aims to automate tedious parts of game development using specialized agents. No concrete results were provided in the paper - someone will need to test this out and report back. Full summary here. Paper is here. submitted by /u/Successful-Western27 [link] [comments]

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Hi! I'm trying to jazz up my design portfolio for applying for jobs, and I wanted to insert some cute illustrations on each project page. The projects deal with a variety of topics so I'll need pictures of many things, but want to keep the style quite consistent. What is the best AI tool right now to do this? I paid for Midjourney but I can't seem to understand how to get it to do this. For example I got this image from DALLE and love the style, the white background also helps make it look better on the portfolio. I'd want another image in the same style of two kids throwing a ball, but can't figure out how to do it. Alternatively if I could upload this image to an AI and say "in the same style, generate..." that would be great too. Thank you! submitted by /u/_Dip_ [link] [comments]

submitted by /u/Agitated-Spell3979 [link] [comments]

Currently, we are building a community that is specifically dedicated to Open-Source AI Code Generation Models. Our aim is to create a thriving ecosystem where developers, enthusiasts, and experts can come together to drive innovation, share insights, and promote a collaborative approach to AI code generation. I wanted to provide you with an overview of the key features we're integrating into this community: 1. Collaboration: A dedicated space where enthusiasts and experts alike can collaborate on projects, share their findings, and work on enhancing existing models. 2. Discussion: Whether through forums or chat platforms, we aim to foster discussions around the challenges, breakthroughs, and best practices in the realm of AI code generation. 3. Resource Sharing: Our community will feature a repository/platform for members to freely share and access open-source models, datasets, and other essential tools. With your experience and insight into the AI domain, we would greatly appreciate your feedback on the following:- - Do you believe such a community would be valuable to you personally or to the wider developer community? - Would you consider becoming a part of such a community? - You are already a part of such a community and this one might not be of much value to you? - Any other suggestions or feedback? Your candid feedback on this idea, its potential impact, and any suggestions you might have will be invaluable to us as we continue shaping this community's structure and offerings. submitted by /u/akanshtyagi [link] [comments]

The Biden administration is considering closing a loophole that gives Chinese companies access to American artificial intelligence (AI) chips through units located overseas. The United States previously restricted shipments of AI chips to China but left overseas subsidiaries of Chinese companies with unfettered access. The Biden administration is now looking for ways to close this loophole and prevent China from accessing top AI technology. However, it is challenging to plug every gap in export controls. Chinese firms are purchasing chips for use in data centers abroad, and it is difficult for the United States to police those transactions. The United States has been seeking to halt the rise of China's AI capability, which depends on its access to U.S. chips. Washington has been working to close other loopholes that allow AI chips into China, and the new rules expected this month will likely apply those same restrictions more broadly to all companies in the market. The U.S. government is also grappling with the issue of Chinese parties accessing U.S. cloud providers like Amazon Web Services. Overall, the Biden administration is facing challenges in cutting China off from top AI technology and closing all loopholes in export controls. Source : https://www.reuters.com/technology/biden-eyes-adding-ai-chip-curbs-chinese-companies-abroad-2023-10-13/ submitted by /u/NuseAI [link] [comments]

I want to sort folders of pictures of people that are similar to an input photo by similarity. I managed to use DeepFace but I'm wondering if anyone knows a better method? ​ submitted by /u/RedditAlreaddit [link] [comments]  ( 9 min )

Paper: https://arxiv.org/abs/2309.17382 Project page: https://agentification.github.io/RAFA Code: https://github.com/agentification/RAFA_code Reason for future, act for now (RAFA) TL;DR: - The first autonomous LLM agent RAFA with provable regret guarantees and outstanding empirical performances. - SOTA results on Game of 24, ALFWorld, BlocksWorld, and Tic-Tac-Toe. submitted by /u/WolverineUnable5957 [link] [comments]  ( 9 min )

submitted by /u/shaongit [link] [comments]  ( 8 min )

So, a few weeks ago, I got interested in the exploration problem in Reinforcement Learning and came across this amazing paper. Just wanted to know if any of you came across any paper which explores this idea more or takes it forward. Thanks in advance. submitted by /u/Interesting-Weeb-699 [link] [comments]  ( 9 min )

Hey folks, I developed a research tool https://idea-factory.ngrok.dev/ (Login: [temp@holistic-intelligence.net](mailto:temp@holistic-intelligence.net) Password: noidea) to identify novel research problems grounded in the scientific literature. Given an idea that intrigues you, the tool identifies the most relevant pieces of literature, creates a brief summary, and provides three possible extensions of your idea. I would be happy to get your feedback on the usefulness of them. Thank you in advance! submitted by /u/Ma7dy [link] [comments]  ( 9 min )

submitted by /u/Animated-AI [link] [comments]  ( 8 min )

What is the correct pipeline for data processing when conducting time series forecasting? Should we begin with data normalization/standardization, followed by feature selection, and then split the data into training, validation, and test sets? Or is it advisable to initially split the data to prevent spill-over effects? I'm concerned about the possibility of training my model on (part of) the test data, which could result in spill-over effects. However, if the recommended approach is to split the data first and then perform normalization and feature selection, what impact would this have on the selected features? Does the manner in which we split the data into random time periods matter, or is it necessary to incorporate a validation method that accounts for temporal effects? I'm worried that the selected features might depend on the time period I choose for my training and test sets. What is the best practice in this scenario? submitted by /u/Ambitious-Pay6329 [link] [comments]  ( 9 min )

Game dev is super complex nowadays - games have huge codebases, massive teams, and dev cycles dragging on for years. Costs are insane too - budgets can hit $100M+ easily. In a new paper, researchers propose to reverse this trend with an AI framework called GameGPT that automates parts of the dev process using multiple AI agents. Each agent handles a different role (all are fine-tuned from relevant base models): One agent reviews the game design plan to catch errors Another turns tasks into code implementations Reviewer agents check the code and results A testing agent validates everything works as expected By breaking up the workflow, GameGPT can simplify things for the AI agents. They just focus on a narrow role versus having one jack-of-all-trades agent. The authors argue GameGPT can eliminate repetitive and rote elements of gamedev like testing. This would free up developers to focus on creative design challenges. However, the GameGPT paper does not include any concrete results or experiments demonstrating improved performance. There is no evidence presented that GameGPT reduces hallucinations, redundancy or development time. The authors mention empirical results support their claims that the architecture is more effective, but none are provided. I could not find any additional support material about this work, like a project website, that I could use to further check into this (maybe someone can share in the comments?). Right now GameGPT seems mostly conceptual. The ideas are interesting but hard to assess without quantitative results. TLDR: New GameGPT AI framework aims to automate tedious parts of game development using specialized agents. No concrete results were provided in the paper - someone will need to test this out and report back. Full summary here. Paper is here. submitted by /u/Successful-Western27 [link] [comments]  ( 9 min )

hi, I would like to create a system that generate different audio samples, based on an audio sample prompt. Does anyone know whether such a project or similar ideas have been already implemented? Or any suggestion on what to read in order to realize such a project? I have knowledge in ML programming and python audio generation. submitted by /u/busconw [link] [comments]  ( 9 min )

Hi, so I've been working with NanoGPT, finetuning GPT-2, and I'm getting terrible MFUs, with 5 warmup steps at -100% and normal steps have an MFU of around 3-4%. Most runs I hear of have an MFU at around 45%? How do get this better? Colab -> https://colab.research.google.com/drive/1gvTsyjxHiDkKHFsnWWouzr1xJWW23BA3?usp=sharing Code -> https://github.com/VatsaDev/NanoPhi2 submitted by /u/vatsadev [link] [comments]  ( 9 min )

https://medium.com/@rishiswethan.c.r/how-gpt-4v-ision-will-revolutionise-image-annotation-b0d3ace64bff?source=friends_link&sk=4be42541a8a8ee40e18ef14533342cfd submitted by /u/Remarkable_Seesaw_89 [link] [comments]  ( 8 min )

I have tired barracuda, vuforia and it doesn’t work for some reason. And completely lost atm. It’s an object detection model to detect the circuit schematic symbols using computer vision submitted by /u/PreferenceFrosty2958 [link] [comments]  ( 9 min )

Fine-tuning large language models with the aim of obtaining a small but accurate model is extremely difficult. This is because you have to strike a balance between the model’s size and accuracy. Researchers from IST Austria & Neural Magic seem to have found a sweet spot. In their latest paper, they successfully applied sparse fine-tuning on MPT with remarkable performance. The MPT model was pruned to 75% without a drop in accuracy, showing performance that is on-par with quantization approaches. Particularly, the resulting sparse model can execute fast on CPUs by taking advantage of the sparsity. Instead of performing standard loss-based fine-tuning which may fail to recover accuracy, the researchers experiment with distillation-type losses. These losses are better at recovering accuracy at high sparsity. What’s impressive is that the sparse fine-tune LLM can achieve 7.7 tokens per second on a single core and 26.7 tokens per second on 4 cores of an cheap consumer AMD Ryzen CPU. The MPT-7B model was fine-tuned via SFT obtaining a dense baseline that showed remarkable performance. This baseline was later pruned with SparseGPT to 40% to 80% reaching 5X compression ratios. By applying SquareHead KD, FP32 models with 75% can be obtained with NO accuracy loss, outperforming cross-entropy and other KD methods. The paper is available on Arxiv. Sparse Finetuning for Inference Acceleration of Large Language Models: https://huggingface.co/papers/2310.06927 MPT Sparse Finetuned on GSM8k with DeepSparse Hugging Face Space: https://huggingface.co/spaces/neuralmagic/sparse-mpt-7b-gsm8k submitted by /u/mwitiderrick [link] [comments]  ( 9 min )

submitted by /u/hungryillini [link] [comments]  ( 8 min )

Paper: https://arxiv.org/abs/2310.06775 GitHub: https://github.com/daveshap/ACE_Framework Blog post: https://medium.com/@dave-shap/autonomous-agents-are-here-introducing-the-ace-framework-a180af15d57c Abstract: The rapid development and adoption of Generative AI (GAI) technology in the form of chatbots such as ChatGPT and Claude has greatly increased interest in agentic machines. This paper introduces the Autonomous Cognitive Entity (ACE) model, a novel framework for a cognitive architecture, enabling machines and software agents to operate more independently. Drawing inspiration from the OSI model, the ACE framework presents layers of abstraction to conceptualize artificial cognitive architectures. The model is designed to harness the capabilities of the latest generative AI technologies, including large language models (LLMs) and multimodal generative models (MMMs), to build autonomous, agentic systems. The ACE framework comprises six layers: the Aspirational Layer, Global Strategy, Agent Model, Executive Function, Cognitive Control, and Task Prosecution. Each layer plays a distinct role, ranging from setting the moral compass and strategic thinking to task selection and execution. The ACE framework also incorporates mechanisms for handling failures and adapting actions, thereby enhancing the robustness and flexibility of autonomous agents. This paper introduces the conceptual framework and proposes implementation strategies that have been tested and observed in industry. The goal of this paper is to formalize this framework so as to be more accessible. ​ https://preview.redd.it/7scnwk5a5dub1.png?width=850&format=png&auto=webp&s=371b5b02a453dcad3e70a2600cc2d625eda44133 ​ submitted by /u/Prior-Travel3670 [link] [comments]  ( 9 min )

Hey folks, I watched a YouTube video, about how some LLMs tokenise languages other than English. For example for the Greek language you will see that this is failing totally, as one character is one token always: ​ https://preview.redd.it/835p97cyhcub1.png?width=1900&format=png&auto=webp&s=944b150cc0fc112cb8cd2bac600f6fcdcc85fb1e My question is, if I would fine-tune it with Alpaca Lora based on Greek text, would the tokeniser change and work properly? Or the fine tune would not work as the tokeniser cannot be retrained/tuned? submitted by /u/kostakos14 [link] [comments]  ( 9 min )

Hello, I’m a 3rd year data science and linguistics major at a top 30 school looking to land an internship at industry research. I’d say I’m fairly competitive. Extensive research experience. 2nd author at EMNLP, and did an REU at a prestigious institute. I’m already looking at some places such as AI2, but I’m curious if there are other internships I should be aware of. submitted by /u/Kai_151 [link] [comments]  ( 9 min )

submitted by /u/fromnighttilldawn [link] [comments]  ( 8 min )

I've been trying a variety of pre-constructed and self-made U-net-like CNNs. Had a few questions: When using torch summary, is there a general formula for estimating a model's inference time/backprop time and required GPU ram based on the information torch summary gives ( Total params, Trainable params, Non-trainable params, Total mult-adds (G), Input size, Forward/backward pass size (MB), Params size (MB), Estimated Total Size (MB)), and other hyper-parameters such as batch size? Why is my self-made model (which has smaller quantities in all the parameters torch summary outputs) requiring more GPU ram AND taking more time for inference and backprop? Is the coding style for the model's class and its forward prop a huge factor here? If so, could you please provide tips for making my code more efficient? Here's the notebook showcasing a pre-made model from MONAI and two of my self-made models: https://colab.research.google.com/drive/1VRrdnzaAbp25_DtaWTKHW5JxzyhmueMC?usp=sharing I've also listed some of my observation on the models and their results in the notebook. Any ideas or suggestion would be much appreciated. submitted by /u/mimivirus2 [link] [comments]  ( 9 min )

submitted by /u/squirrels-api [link] [comments]  ( 9 min )

Deep reinforcement learning has led to a variety of compelling results. However, performance issues, particularly relating to the data efficiency of simulation has limited it applicability in domains where simulations run more slowly. Our solution is to use a logic base framework, PyReason, as a proxy for the simulation. ​ https://preview.redd.it/kdhpu9qraaub1.png?width=1786&format=png&auto=webp&s=8155ba38fc66bd3a2fe934b1f395351c4db68e2f We showed that inference with PyReason logic program can provide up to a three order-of-magnitude speedup when compared with native simulations (we studied AFSIM and Starcraft2) while providing comparable reward and win rate (we found that PyReason-trained agents actually performed better than expected in both AFSIM and Starcraft2). ​ https://preview.…  ( 9 min )

i am a little puzzled, i know that transformers is the HF framework/library to load infere and train models easily and that llama.cpp is another framework/library that does the more of the same but specialized in models that runs on CPU and quanitized and run much faster i understand that GGML is a file format for saving model parameters in a single file, that its an old problematic format, and GGUF is the new kid on the block, and GPTQ is the same quanitized file format for models that runs on GPU ​ so here is what i can't understand (assuming i got all the rest correct): does HF Transformers support loading GGUF or GGML models ? and does GGUF needs a tokenizer json or does the data comes from within the gguf file itself and is safetensors (another file format) supported by both Transformers and Llama.cpp ​ since i cannot find python examples for these combination i assume all the answers are - No ​ can anyone shed some light ? submitted by /u/Particular_Flower_12 [link] [comments]  ( 9 min )

I was following Nicholas Renotte's RL in 3 hours tutorial and I ran into this issue at time stamp 1:10:00 while testing my trained Agent. ValueError: setting an array element with a sequence. The requested array has an inhomogeneous shape after 1 dimensions. The detected shape was (2,) + inhomogeneous part. This is my code for testing my environment: episodes=5 for episode in range(1,episodes+1): obs=env.reset() done=False score=0 print(obs) while not done: env.render() action, _ = model.predict(obs) #Now using model here obs, reward, done, truncated, info = env.step(action) score += reward print('Episode:{} Score:{}'.format(episode,score)) env.close() And this is the environment I am using : environment_name = 'CartPole-v1' env=gym.make(environment_name,render_mode="human") the model variable has my trained model stored in it and is initialized as such : model =PPO.load(PPO_Path, env=env) The print(obs) function returns this value : (array([ 0.03954345, -0.04975226, -0.02942382, -0.02261402], dtype=float32), {}) I am running this code in a Notebook on VS code on an M2 Macbook running MacOS 13.5, I am using Python 3.9.15 and the latest version of all the other libraries and dependencies. Please help submitted by /u/Straight-Knowledge83 [link] [comments]  ( 9 min )

I'm doing a project that aims to use reinforcement learning (PPO variations) with UAVs. What are the most up to date tools are for implementing and trying new RL algorithms in this space? I've looked at AirSim, and it seems to no longer be supported by Micrsosoft. I've also been heavily looking at Flightmare, which is almost exactly what I want, but getting the tool that hasn't been maintained for years up and running is giving me headaches (and the documentation is not great/up to date either). Ultimately, what I'm looking for is: * Physics simulation * Photo-realistic vision * Built-in integration with Gym would be awesome * Python platform preferred, C++ also ok I've also used ROS/Gazebo with PyTorch previously, and that is my backup plan I suppose, but it's not photo-realistic and is kind of slow in my experience. submitted by /u/zeus_the_transistor [link] [comments]  ( 9 min )

Hello everyone, I'm a Data Science student diving into an exciting thesis topic: using reinforcement learning to stabilize boats in rough seas by adjusting a keel's angle. But I am a bit concerned about the high complexity of the problem and the given situation: Action Space: Continuous, representing the keel's angle adjustments. State Space: Continuous, capturing the dynamic behavior of the sea, including waves. Training Environment: Currently, the company only has a real-world water tank setup to simulate the sea conditions. There's no computer simulation available. Given this setup, I have a couple of concerns: Is it possible to train an RL model effectively in such a complex real-world scenario without first having a computer simulation? And if yes, what would be your initial steps in doing so? Are there possibilities to reduce the problem's complexity while training exclusively in the real-world water tank simulation? (i.e. transforming the action space into a discrete action space?) Any insights or advice would be greatly appreciated! submitted by /u/No-Wasabi3556 [link] [comments]  ( 9 min )

Deep reinforcement learning has led to a variety of compelling results. However, performance issues, particularly relating to the data efficiency of simulation has limited it applicability in domains where simulations run more slowly. Our solution is to use a logic base framework, PyReason, as a proxy for the simulation. ​ https://preview.redd.it/6wmg0qnlaaub1.png?width=1786&format=png&auto=webp&s=01f82cf24de79b317b6f9406b0b6379b949a34d3 We showed that inference with PyReason logic program can provide up to a three order-of-magnitude speedup when compared with native simulations (we studied AFSIM and Starcraft2) while providing comparable reward and win rate (we found that PyReason-trained agents actually performed better than expected in both AFSIM and Starcraft2). ​ https://preview.redd.it/u8f44fskaaub1.png?width=1636&format=png&auto=webp&s=9509f03a936f41cd0131388564833b86a39c295a However, the benefits of our semantic proxy go well beyond performance. The use of temporal logic programming has two crucial beneficial by-products such as symbolic explainability and modularity. PyReason provides an explainable symbolic trace that captures the evolution of the environment in a precise manner while modularity allows us to add or remove aspects of the logic program – allowing for adjustments to the simulation based on a library of behaviors. PyReason is well-suited to model simulated environments for other reasons – namely the ability to directly capture non-Markovian relationships and the open-world nature (defaults are “uncertain” instead of true or false). We have demonstrated that agents can be trained using standard RL techniques such as DQN using this framework. Preprint: https://arxiv.org/abs/2310.06835 Video: https://youtu.be/9e6ZHJEJzgw Code for PyReason-as-a-Sim (integration with DQN): https://github.com/lab-v2/pyreason-rl-sim Code for PyReason Gym: https://github.com/lab-v2/pyreason-gym PyReason Home: neurosymbolic.asu.edu/pyreason/ ​ submitted by /u/Neurosymbolic [link] [comments]  ( 9 min )

I made a environment with piece wise constant reward function for testing the network architecture. And its episode length is 1. The critic will try to learn this and become a piecewise constant function. And have a gradient close to 0 making the gradient vanish for the policy. I can think of some solutions: - Change the reward function to a dense reward But i wanted some other views; has anyone solved such problems? submitted by /u/Automatic-Web8429 [link] [comments]

I am trying to read this paper and I am unable to get the big picture over here. Can someone please explain what's going on in the Propagation and Planning stage? In the Model stage, I understand that they are using a Probabilistic Model to handle uncertainty. ​ https://preview.redd.it/idenqd492aub1.png?width=945&format=png&auto=webp&s=40da9bf53b21dbed63b70571f3833b0fe3a9dabb For instance, what does Particle mean in this paper? This big picture here is that I am trying to understand the Model Based Policy Optimization paper and it seemed like they built upon the above paper. submitted by /u/Academic-Rent7800 [link] [comments]

Deep reinforcement learning has led to a variety of compelling results. However, performance issues, particularly relating to the data efficiency of simulation has limited it applicability in domains where simulations run more slowly. Our solution is to use a logic base framework, PyReason, as a proxy for the simulation. ​ https://preview.redd.it/pmukb2k7aaub1.png?width=1786&format=png&auto=webp&s=3fb36d0fbeb75393ae8f71f8f369ff5e0b79fbcb We showed that inference with PyReason logic program can provide up to a three order-of-magnitude speedup when compared with native simulations (we studied AFSIM and Starcraft2) while providing comparable reward and win rate (we found that PyReason-trained agents actually performed better than expected in both AFSIM and Starcraft2). ​ https://preview.…

Hi guys, I'm hoping for advice on the direction to detect detect pattern/ anomaly at small scale. I understand there are certain tools out there for webpage monitoring, but let's say this is just an example that I'm ingesting small amount of hourly/daily traffic to a sub webpage on my site (anywhere from 50-100 visits per day, this may mean max ~30 visits/per hour) There are times when traffic to the page drops as the page doesn't fully load , or the other page on which I'm hosting the link to this page doesn't load resulting in people can't see the link tothis sub page). Giving the scope/scale of this, amount of the data, it's not possible for me to use other solutions for anomaly detection (those that costs like $100-$1000+/month) and I'm not sure where to start with ML with this minimal amount of hourly/daily data to monitor. Is there anything that I should look into? Thank you submitted by /u/duyth [link] [comments]  ( 9 min )

Came across this post https://community.openai.com/t/foundational-must-read-gpt-llm-papers/197003 As I am new to LLM's , Please share your thoughts on how to start and what subtopics to learn in depth ? ​ submitted by /u/Electrical_Study_617 [link] [comments]  ( 8 min )

Having jumped into AI this last year I've used Google AutoML a lot and it's honestly worked great. I primarily use it for text classification. Training usually takes anywhere from 4-8 hours. The results have been above 90% accurate on interference. Now, the problem. Cost. It's super expensive to run an endpoint for predictions with Google AutoML, for text classification. I'm wondering if anyone has any alternatives or ideas for similar results for cheaper. I am ok waiting for prediction results a bit as I don't need sub 1ms type responses lol. But everything I've tried has yielded less then optimal results. Tried various hugging face models, and accuracy is about 50%. submitted by /u/zepaz [link] [comments]  ( 9 min )

Paper: https://arxiv.org/abs/2310.08588 Blog: https://choiszt.github.io/Octopus/ Github: https://github.com/dongyh20/Octopus Youtube short: https://www.youtube.com/watch?v=lHbTvB0yIP4 Abstract: Large vision-language models (VLMs) have achieved substantial progress in multimodal perception and reasoning. Furthermore, when seamlessly integrated into an embodied agent, it signifies a crucial stride towards the creation of autonomous and context-aware systems capable of formulating plans and executing commands with precision. In this paper, we introduce Octopus, a novel VLM designed to proficiently decipher an agent's vision and textual task objectives and to formulate intricate action sequences and generate executable code. Our design allows the agent to adeptly handle a wide spectrum of tasks, ranging from mundane daily chores in simulators to sophisticated interactions in complex video games. Octopus is trained by leveraging GPT-4 to control an explorative agent to generate training data, i.e., action blueprints and the corresponding executable code, within our experimental environment called OctoVerse. We also collect the feedback that allows the enhanced training scheme of Reinforcement Learning with Environmental Feedback (RLEF). Through a series of experiments, we illuminate Octopus's functionality and present compelling results, and the proposed RLEF turns out to refine the agent's decision-making. By open-sourcing our model architecture, simulator, and dataset, we aspire to ignite further innovation and foster collaborative applications within the broader embodied AI community. https://preview.redd.it/1zn9q3g7a8ub1.jpg?width=1651&format=pjpg&auto=webp&s=3b14f862b24784918d6b4514bf575cf29bc65edf https://preview.redd.it/sv2y06g7a8ub1.jpg?width=1079&format=pjpg&auto=webp&s=be9ab7dd7cf23018b6d1fa0c584ad301b04c8abf https://preview.redd.it/350xc6g7a8ub1.jpg?width=942&format=pjpg&auto=webp&s=53e57541d35ca23d06b8c5be71c2b0c1910fdf90 ​ submitted by /u/Singularian2501 [link] [comments]  ( 9 min )

A Complete Guide to LLMs-based Autonomous Agents (Part I): https://medium.com/p/69515c016792 My article offers a comprehensive overview of LLM-based agents, covering Chain of Thought, Plan and Solve/Execute, Self-Ask, ReAct, Reflexion, Self-Consistency, Tree of Thoughts, and Graph of Thoughts. It traces their evolution from basic forms, driven primarily by prompt engineering, to advanced models that emulate human problem-solving intricacies. Moreover, it provides an engineer's insights into the architecture behind these autonomous agents. Naturally suitable for AI agent: LLMs feature a natural language interface tailored for user-computer interactions and they come equipped with innate reasoning abilities. LLM's Deficiency: Despite its strengths, GPT-4 can provide incorrect answers or hallucinations for complex tasks. Challenges with Training: Finetuning pretrained LLMs doesn't enhance reasoning capabilities. While creating a larger LLM can bolster its problem-solving skills, the process can span several months to a year, potentially leading to a two-year wait before its official launch. Closed Model and RAG: LLMs, once trained, are unable to fetch real-time data and have inherent shortcomings. However, for Q&A tasks, leveraging an open-book method proves more effective. The aim is not to have an all-knowing model but one skilled in reasoning and utilizing tools. LLM Agent Approach: We direct LLMs to break down intricate tasks, tackle individual sub-tasks, evaluate them, and make revisions of the strategy as needed. submitted by /u/Appropriate-Map-9923 [link] [comments]  ( 9 min )

In my last semester and we have to pick a topic related to big data analytics. Right now I have to prepare a proposal for my topic. My topic will have to do with something to ML and the medicinal field. Current plan: Get a dataset related to my topic. Right now its Parkinson's disease. My question is, for the dataset would I need a dataset with text data or would images of scans of the brain be better for detecting say early detection be better? I cant figure out which would be the better dataset. Get the dataset and then use Azure machine learning to prepare my dataset and do some data cleaning and handling and then get a model out of it. I picked azure because I have azure license from my uni and after searching about, I read about the azure machine learning service. Would azure be a good choice for training my model on this task? I've mostly used google colab for training small models. Once the model is trained and setup. I want to setup a front end web app (flask) and then setup my model so that users can upload either text data or image scans and then model would output results regarding the inputted data. My question is, would it be ideal to have the model located on my local machine or would azure let me do api calls between my local to the azure trained model? Would all this be feasible to do? I'm not looking to develop a full fledge application, just want to create a model with a dataset of images or text and then be able to feed new images to the trained model and get an output. Just looking for opinions or advice on this topic. Thanks. submitted by /u/Jesustakethewheeeeel [link] [comments]  ( 9 min )

I read the previous discussion on whether a PhD is required in the field, and I had a follow-up question: does the topic of your PhD matter? So let’s say you finish a PhD in the field of medical machine learning (non-CV), would an automotive company, FAANG, or e.g. DeepMind still like to hire you once you would like to switch your sub-field a bit? Or are you simply less desirable than a candidate without a PhD but more experience in CV? I am asking this because I would like to stay flexible as I have many ML sub-fields I want to work in, and I do not want to limit my options by pursuing a PhD in a topic that I don’t want work in for my entire life. For context, I do already have 2 years working experience as an AI engineer and I am finishing my AI master’s. submitted by /u/Otoz123 [link] [comments]  ( 9 min )

I'm planning on creating my own AI voice to use with ChatGPT. I have done my research, and there are two ways to achieve a quality TTS model to use. I have tried them both. I fine-tuned tortoise tts on my own 20-minute dataset. I have also tried to create a model using Tacotron2 and the dataset. The quality of the fine-tuned model is better. But one downside is that I still have to give the fine-tuned tortoise model a reference voice for it to choose the voice that I fine-tuned it with. On the other hand, the trained model didn't need to. The question here is: why didn't the tortoise model choose the voice in the dataset as the default? Do I need to expand my dataset for it to be chosen as the main voice? ​ Thank all. submitted by /u/Capital_Birthday_654 [link] [comments]  ( 9 min )

Do pretrained Transformers Really Learn In-context by Gradient Descent? https://x.com/Shadowkiller331/status/1713003711629516862?s=20 ​ https://preview.redd.it/zpwkh47hm7ub1.png?width=450&format=png&auto=webp&s=6def807c9c9f605e3f7839159db3402d837f6895 submitted by /u/Educational-Newt2052 [link] [comments]  ( 8 min )

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We’ve been diving deep into foundational papers on Fridays as a group. It’s been helpful for us to get into the nitty gritty details of these papers, so hope you find it helpful too. Would love to have anyone join the discussion next week! submitted by /u/FallMindless3563 [link] [comments]  ( 9 min )

What can this mean to artificial neural networks? submitted by /u/hhh888hhhh [link] [comments]  ( 8 min )

Hi all, I did a fine tune of CodeLlama-7b on a custom dataset and I was getting very excited because it was doing very well on evals. I saved the model with model.save() and model.push_to_hub() and it seemed to work. When I load the model it shows the structure with the Lora_A and Lora_B for every layer, but it now acts like the base model with no changes. Is it possible I saved wrong or likely that I am loading wrong? Any help is greatly appreciated! submitted by /u/cstein123 [link] [comments]  ( 9 min )

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Hi there, I'm new to Reinforcement Learning (RL), and the papers I've come across mainly focus on scenarios where states change with choices in a game. However, I'm interested in finding the best RL algorithm for a simpler case. I have an input I and a policy P. P outputs probabilities for available choices (a limited set of integers), and a reward r is given for each choice (the reward is costly to compute that’s why I use RL). The goal is to train P to maximize the reward. So as if we are in a game that ends after only one choice. Any recommendations for the best RL algorithm in this case? Thanks! submitted by /u/Meddhouib10 [link] [comments]  ( 9 min )

I recently graduated with my bachelor's from a low ranked school with a good GPA. I was planning on starting a PhD studying NLP and applied to 12 mid level schools. However, I was unfortunately rejected from all the schools I applied to. I suspect it was likely due to my lack of experience in NLP research as my school didn't have any professors who do research in that area. My current plan is to work in industry for the next two years and try and do some NLP research on the side before reapplying. Do any NLP labs allow for external volunteer researchers? Besides that, are there any other ways to get research experience? submitted by /u/Bananas970 [link] [comments]  ( 9 min )

Hi, I've been using the Huggingface library to fine-tune the Whisper model. While the WER was initially decreasing, I've noticed it began to rise even though the validation loss continues to drop. Could the issue be related to my testing on a very small dataset? As shown in the image, after 80th step the wer suddenly started increasing from 13 -> 28 https://preview.redd.it/xq2bm0oyh5ub1.png?width=838&format=png&auto=webp&s=136447f527bea6880b46ae588463500304b1d6bb ​ submitted by /u/aadityaura [link] [comments]  ( 9 min )

Yo! I have some images I curated on MJ, I want to run these together into an AI and spit out more outputs like these. The current process has me get maybe .2% successful outputs through MJ I figure the next step to more outputs is training a custom model. What's the easiest way to do this using a web-based API? Does this involve using Stable Diffusion? submitted by /u/AdministrativePie991 [link] [comments]  ( 9 min )

Hey 😀 not sure if extremely trivial or really tricky. In the end, I want a machine that generates a time series without further input based on training data, generating a new time series every time. I want this to be based on a transformer. I want it trained with data looking like this: 2023-07-03 14:19:48,GOOD 2023-07-04 13:59:07,GOOD 2023-07-05 01:58:54,GOOD 2023-07-05 03:30:05,BAD 2023-07-05 05:17:43,BAD 2023-07-06 05:35:34,GOOD 2023-07-07 14:06:03,GOOD 2023-07-08 21:16:05,BAD with “GOOD” and “BAD” being the state of the system which is likely dependent on the time series data up to that point. I have a lot of data and it’s data points like the one above with maybe a hundred rows of data on average for a few thousand systems. Every system is independent of all others but all are identical. I do not want to train only on “GOOD” as this would leave out a lot of valuable data … Is there a way to train a time series transformer with both data that leads to GOOD as well as BAD outcomes, so it would generate time series from scratch that are unlikely to have BAD outcomes? Thank you!! submitted by /u/_VeniVidiVeni_ [link] [comments]  ( 9 min )

Hey r/MachineLearning! 🚀 Have you ever been frustrated with the lengthy and sometimes cumbersome TensorFlow code for defining models? Or wished you could experiment with different architectures without dealing with copious lines of code? That's where VGSLify steps in. Why Use VGSLify? Compact Definitions: VGSLify leverages VGSL spec, enabling you to express intricate model architectures in a compact and elegant manner. This means you can quickly experiment with different models by simply tweaking a string format, bypassing the verbose code traditionally required. Swift Prototyping: Craft intricate neural network architectures using succinct VGSL spec strings, allowing you to iterate faster and more efficiently. From TensorFlow to VGSL: Got a pre-existing TensorFlow model? Easily co…  ( 9 min )

I'm 21 years old and currently pursuing a master's degree in theoretical physics in the UK. I have a strong interest in machine learning and have completed many computing courses as well as independent projects in this field. I'm considering a career in machine learning and I'm curious about the benefits of doing a PhD. I've heard that the salary difference may not be substantial. Could anyone provide insights on how important a PhD is for specific roles in this field? Additionally, what factors should I consider when deciding whether to pursue a PhD in machine learning, apart from my passion for ML? Also are private PhDs common in ML. Working in a company and asked them to pursue a PhD within the company? Thanks :) submitted by /u/Neat-Print2792 [link] [comments]  ( 9 min )

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i am trying to detect code segments in a text response of an LLM, so i can highlight them using Highlight,JS, ​ is there a good model that can do the classification of a block of text and decide if it is a block of code or a block of NLP simple text (english) ? submitted by /u/Particular_Flower_12 [link] [comments]  ( 9 min )

I am a multi-time founder myself. I've secured millions from investors for my past startups and had notable success with a video app that gathered 4M users and $300k in revenue. However, due to the intense competition in the video app editing sector, my team and I couldn't turn a profit. After my last startup faltered during the covid period, I transitioned to being a full-time product-market fit and growth marketing consultant and have made really great money doing it. I assist new startups in avoiding the mistakes I made and implement frameworks that significantly increase their chances of success. I've observed that many new founders venture into startups without fully grasping the challenges of building something people genuinely desire. It’s really not easy. How would you know what y…

A free AI image detector is being used to discredit a photograph of a burnt corpse of a baby killed in Hamas's attack on Israel. However, experts have pointed out that the image does not show any signs of being created by AI. The idea that the image is AI-generated has spread on Twitter, suggesting that official Israeli accounts are spreading AI-generated misinformation. AI image generators have trouble replicating reality accurately, and the shadows in the photograph are consistent with a real image. Multiple AI image detection tools have also determined that the image is not AI-generated. Source : https://www.404media.co/ai-images-detectors-are-being-used-to-discredit-the-real-horrors-of-war/ submitted by /u/NuseAI [link] [comments]

Hello everyone! 🌟 I hope this post finds you well. I've been delving deeper into the world of AI code generation recently and am curious to discover if there are communities or platforms specifically dedicated to open-source AI code generation models. I'm aware of hugging face but is there any other besides that. Here's what I'm looking for: Collaboration: A space where enthusiasts and experts alike can collaborate on projects, share insights, and improve upon existing models. Discussion: Forums or chat platforms where discussions around the challenges, breakthroughs, and best practices in AI code generation take place. Resource Sharing: A repository or platform where open-source models, datasets, and related tools can be freely shared and accessed. Learning and Tutorials: Any resources that can help newcomers grasp the concepts and intricacies of AI code generation. If you know of any such community or are part of one, please do let me know. submitted by /u/akanshtyagi [link] [comments]

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does anyone know if there is an AI online where you can import documents and the AI is forming and asking you questions about that topic on the document? like an AI who generates test for you to be prepared for every potential question in a school test. submitted by /u/satanskittenz [link] [comments]

Ok so we have AI generated content, First text, then images, then videos. What will the world look like when we have a generative world? Generative objects, Generative Games, Generative Moods, Generative memories, Generative senses and perceptions, Generative Environments, Generative Reality. Anyone want to talk about what it might look like? ( I would like to hear a unhinged idea for what might happen, Speculative of course ) submitted by /u/rolyataylor2 [link] [comments]

The ability of Generative AI (GenAI) tools to deliver accurate and reliable outputs entirely depends on the accuracy and reliability of the data used to train the Large Language Models (LLMs) that power the GenAI tool. Unfortunately, the Law of GIGO – Garbage In, Garbage Out – threatens the widespread adoption of GenAI.  Whether generating… Read More »AI’s Kryptonite: Data Quality The post AI’s Kryptonite: Data Quality appeared first on Data Science Central.  ( 22 min )

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Let Q be a convex quadrilateral with at most two parallel sides. Draw the two diagonals then draw a line through their midpoints. This line is called the Newton line. (The requirement that at most two sides are parallel insures that the midpoints are distinct and so there is a unique line joining them.) In […] Newton line first appeared on John D. Cook.  ( 5 min )

Modeling the interaction between proteins and ligands and accurately predicting their binding structures is a critical yet challenging task in drug discovery. Recent advancements in deep learning have shown promise in addressing this challenge, with sampling-based and regression-based methods emerging as two prominent approaches. However, these methods have notable limitations. Sampling-based methods often suffer from low efficiency due to the need for generating multiple candidate structures for selection. On the other hand, regression-based methods offer fast predictions but may experience decreased accuracy. Additionally, the variation in protein sizes often requires external modules for selecting suitable binding pockets, further impacting efficiency. In this work, we propose $\mathbf{FABind}$, an end-to-end model that combines pocket prediction and docking to achieve accurate and fast protein-ligand binding. $\mathbf{FABind}$ incorporates a unique ligand-informed pocket prediction module, which is also leveraged for docking pose estimation. The model further enhances the docking process by incrementally integrating the predicted pocket to optimize protein-ligand binding, reducing discrepancies between training and inference. Through extensive experiments on benchmark datasets, our proposed $\mathbf{FABind}$ demonstrates strong advantages in terms of effectiveness and efficiency compared to existing methods. Our code is available at $\href{https://github.com/QizhiPei/FABind}{Github}$.  ( 2 min )

Maximum-type statistics of certain functions of the sample covariance matrix of high-dimensional vector time series are studied to statistically confirm or reject the null hypothesis that a data set has been collected under normal conditions. The approach generalizes the case of the maximal deviation of the sample autocovariances function from its assumed values. Within a linear time series framework it is shown that Gumbel-type extreme value asymptotics holds true. As applications we discuss long-only mimimal-variance portfolio optimization and subportfolio analysis with respect to idiosyncratic risks, ETF index tracking by sparse tracking portfolios, convolutional deep learners for image analysis and the analysis of array-of-sensors data.  ( 2 min )

Recent advances in machine learning (ML) have shown promise in aiding and accelerating classical combinatorial optimization algorithms. ML-based speed ups that aim to learn in an end to end manner (i.e., directly output the solution) tend to trade off run time with solution quality. Therefore, solutions that are able to accelerate existing solvers while maintaining their performance guarantees, are of great interest. We consider an APX-hard problem, where an adversary aims to attack shortest paths in a graph by removing the minimum number of edges. We propose the GRASP algorithm: Graph Attention Accelerated Shortest Path Attack, an ML aided optimization algorithm that achieves run times up to 10x faster, while maintaining the quality of solution generated. GRASP uses a graph attention network to identify a smaller subgraph containing the combinatorial solution, thus effectively reducing the input problem size. Additionally, we demonstrate how careful representation of the input graph, including node features that correlate well with the optimization task, can highlight important structure in the optimization solution.  ( 2 min )

The rapid advancements in large language models (LLMs) have ignited interest in the temporal knowledge graph (tKG) domain, where conventional carefully designed embedding-based and rule-based models dominate. The question remains open of whether pre-trained LLMs can understand structured temporal relational data and replace them as the foundation model for temporal relational forecasting. Therefore, we bring temporal knowledge forecasting into the generative setting. However, challenges occur in the huge chasms between complex temporal graph data structure and sequential natural expressions LLMs can handle, and between the enormous data sizes of tKGs and heavy computation costs of finetuning LLMs. To address these challenges, we propose a novel retrieval augmented generation framework that performs generative forecasting on tKGs named GenTKG, which combines a temporal logical rule-based retrieval strategy and lightweight parameter-efficient instruction tuning. Extensive experiments have shown that GenTKG outperforms conventional methods of temporal relational forecasting under low computation resources. GenTKG also highlights remarkable transferability with exceeding performance on unseen datasets without re-training. Our work reveals the huge potential of LLMs in the tKG domain and opens a new frontier for generative forecasting on tKGs.  ( 2 min )

Neural combinatorial optimization (NCO) is a promising learning-based approach for solving challenging combinatorial optimization problems without specialized algorithm design by experts. However, most constructive NCO methods cannot solve problems with large-scale instance sizes, which significantly diminishes their usefulness for real-world applications. In this work, we propose a novel Light Encoder and Heavy Decoder (LEHD) model with a strong generalization ability to address this critical issue. The LEHD model can learn to dynamically capture the relationships between all available nodes of varying sizes, which is beneficial for model generalization to problems of various scales. Moreover, we develop a data-efficient training scheme and a flexible solution construction mechanism for the proposed LEHD model. By training on small-scale problem instances, the LEHD model can generate nearly optimal solutions for the Travelling Salesman Problem (TSP) and the Capacitated Vehicle Routing Problem (CVRP) with up to 1000 nodes, and also generalizes well to solve real-world TSPLib and CVRPLib problems. These results confirm our proposed LEHD model can significantly improve the state-of-the-art performance for constructive NCO. The code is available at https://github.com/CIAM-Group/NCO_code/tree/main/single_objective/LEHD.  ( 2 min )

Neural operators as novel neural architectures for fast approximating solution operators of partial differential equations (PDEs), have shown considerable promise for future scientific computing. However, the mainstream of training neural operators is still data-driven, which needs an expensive ground-truth dataset from various sources (e.g., solving PDEs' samples with the conventional solvers, real-world experiments) in addition to training stage costs. From a computational perspective, marrying operator learning and specific domain knowledge to solve PDEs is an essential step in reducing dataset costs and label-free learning. We propose a novel paradigm that provides a unified framework of training neural operators and solving PDEs with the variational form, which we refer to as the variational operator learning (VOL). Ritz and Galerkin approach with finite element discretization are developed for VOL to achieve matrix-free approximation of system functional and residual, then direct minimization and iterative update are proposed as two optimization strategies for VOL. Various types of experiments based on reasonable benchmarks about variable heat source, Darcy flow, and variable stiffness elasticity are conducted to demonstrate the effectiveness of VOL. With a label-free training set and a 5-label-only shift set, VOL learns solution operators with its test errors decreasing in a power law with respect to the amount of unlabeled data. To the best of the authors' knowledge, this is the first study that integrates the perspectives of the weak form and efficient iterative methods for solving sparse linear systems into the end-to-end operator learning task.  ( 3 min )

The exploration of transition state (TS) geometries is crucial for elucidating chemical reaction mechanisms and modeling their kinetics. Recently, machine learning (ML) models have shown remarkable performance for prediction of TS geometries. However, they require 3D conformations of reactants and products often with their appropriate orientations as input, which demands substantial efforts and computational cost. Here, we propose a generative approach based on the stochastic diffusion method, namely TSDiff, for prediction of TS geometries just from 2D molecular graphs. TSDiff outperformed the existing ML models with 3D geometries in terms of both accuracy and efficiency. Moreover, it enables to sample various TS conformations, because it learned the distribution of TS geometries for diverse reactions in training. Thus, TSDiff was able to find more favorable reaction pathways with lower barrier heights than those in the reference database. These results demonstrate that TSDiff shows promising potential for an efficient and reliable TS exploration.  ( 2 min )

Large Language Models (LLMs) have revolutionized natural language processing and demonstrated impressive capabilities in various tasks. Unfortunately, they are prone to hallucinations, where the model exposes incorrect or false information in its responses, which renders diligent evaluation approaches mandatory. While LLM performance in specific knowledge fields is often evaluated based on question and answer (Q&A) datasets, such evaluations usually report only a single accuracy number for the dataset, which often covers an entire field. This field-based evaluation, is problematic with respect to transparency and model improvement. A stratified evaluation could instead reveal subfields, where hallucinations are more likely to occur and thus help to better assess LLMs' risks and guide their further development. To support such stratified evaluations, we propose LLMMaps as a novel visualization technique that enables users to evaluate LLMs' performance with respect to Q&A datasets. LLMMaps provide detailed insights into LLMs' knowledge capabilities in different subfields, by transforming Q&A datasets as well as LLM responses into an internal knowledge structure. An extension for comparative visualization furthermore, allows for the detailed comparison of multiple LLMs. To assess LLMMaps we use them to conduct a comparative analysis of several state-of-the-art LLMs, such as BLOOM, GPT-2, GPT-3, ChatGPT and LLaMa-13B, as well as two qualitative user evaluations. All necessary source code and data for generating LLMMaps to be used in scientific publications and elsewhere is available on GitHub: https://github.com/viscom-ulm/LLMMaps  ( 3 min )

This paper introduces a novel model-agnostic algorithm called adaptive ensemble batch multi-input multi-output conformalized quantile regression (AEnbMIMOCQR} that enables forecasters to generate multi-step ahead prediction intervals for a fixed pre-specified miscoverage rate in a distribution-free manner. Our method is grounded on conformal prediction principles, however, it does not require data splitting and provides close to exact coverage even when the data is not exchangeable. Moreover, the resulting prediction intervals, besides being empirically valid along the forecast horizon, do not neglect heteroscedasticity. AEnbMIMOCQR is designed to be robust to distribution shifts, which means that its prediction intervals remain reliable over an unlimited period of time, without entailing retraining or imposing unrealistic strict assumptions on the data-generating process. Through methodically experimentation, we demonstrate that our approach outperforms other competitive methods on both real-world and synthetic datasets. The code used in the experimental part and a tutorial on how to use AEnbMIMOCQR can be found at the following GitHub repository: https://github.com/Quilograma/AEnbMIMOCQR.  ( 3 min )

Reinforcement Learning (RL) environments can produce training data with spurious correlations between features due to the amount of training data or its limited feature coverage. This can lead to RL agents encoding these misleading correlations in their latent representation, preventing the agent from generalising if the correlation changes within the environment or when deployed in the real world. Disentangled representations can improve robustness, but existing disentanglement techniques that minimise mutual information between features require independent features, thus they cannot disentangle correlated features. We propose an auxiliary task for RL algorithms that learns a disentangled representation of high-dimensional observations with correlated features by minimising the conditional mutual information between features in the representation. We demonstrate experimentally, using continuous control tasks, that our approach improves generalisation under correlation shifts, as well as improving the training performance of RL algorithms in the presence of correlated features.  ( 2 min )

This paper focuses on enhancing Bengali Document Layout Analysis (DLA) using the YOLOv8 model and innovative post-processing techniques. We tackle challenges unique to the complex Bengali script by employing data augmentation for model robustness. After meticulous validation set evaluation, we fine-tune our approach on the complete dataset, leading to a two-stage prediction strategy for accurate element segmentation. Our ensemble model, combined with post-processing, outperforms individual base architectures, addressing issues identified in the BaDLAD dataset. By leveraging this approach, we aim to advance Bengali document analysis, contributing to improved OCR and document comprehension and BaDLAD serves as a foundational resource for this endeavor, aiding future research in the field. Furthermore, our experiments provided key insights to incorporate new strategies into the established solution.

Graph Neural Networks are a natural fit for learning algorithms. They can directly represent tasks through an abstract but versatile graph structure and handle inputs of different sizes. This opens up the possibility for scaling and extrapolation to larger graphs, one of the most important advantages of an algorithm. However, this raises two core questions i) How can we enable nodes to gather the required information in a given graph ($\textit{information exchange}$), even if is far away and ii) How can we design an execution framework which enables this information exchange for extrapolation to larger graph sizes ($\textit{algorithmic alignment for extrapolation}$). We propose a new execution framework that is inspired by the design principles of distributed algorithms: Flood and Echo Net. It propagates messages through the entire graph in a wave like activation pattern, which naturally generalizes to larger instances. Through its sparse but parallel activations it is provably more efficient in terms of message complexity. We study the proposed model and provide both empirical evidence and theoretical insights in terms of its expressiveness, efficiency, information exchange and ability to extrapolate.

Innovative foundation models, such as GPT-3 and stable diffusion models, have made a paradigm shift in the realm of artificial intelligence (AI) towards generative AI-based systems. In unison, from data communication and networking perspective, AI and machine learning (AI/ML) algorithms are envisioned to be pervasively incorporated into the future generations of wireless communications systems, highlighting the need for novel AI-native solutions for the emergent communication scenarios. In this article, we outline the applications of generative AI in wireless communication systems to lay the foundations for research in this field. Diffusion-based generative models, as the new state-of-the-art paradigm of generative models, are introduced, and their applications in wireless communication systems are discussed. Two case studies are also presented to showcase how diffusion models can be exploited for the development of resilient AI-native communication systems. Specifically, we propose denoising diffusion probabilistic models (DDPM) for a wireless communication scheme with non-ideal transceivers, where 30% improvement is achieved in terms of bit error rate. As the second application, DDPMs are employed at the transmitter to shape the constellation symbols, highlighting a robust out-of-distribution performance. Finally, future directions and open issues for the development of generative AI-based wireless systems are discussed to promote future research endeavors towards wireless generative AI (WiGenAI).

In this paper, we propose a fuzzy adaptive loss function for enhancing deep learning performance in classification tasks. Specifically, we redefine the cross-entropy loss to effectively address class-level noise conditions, including the challenging problem of class imbalance. Our approach introduces aggregation operators, leveraging the power of fuzzy logic to improve classification accuracy. The rationale behind our proposed method lies in the iterative up-weighting of class-level components within the loss function, focusing on those with larger errors. To achieve this, we employ the ordered weighted average (OWA) operator and combine it with an adaptive scheme for gradient-based learning. Through extensive experimentation, our method outperforms other commonly used loss functions, such as the standard cross-entropy or focal loss, across various binary and multiclass classification tasks. Furthermore, we explore the influence of hyperparameters associated with the OWA operators and present a default configuration that performs well across different experimental settings.  ( 2 min )

Most packages for the analysis of fMRI-based functional connectivity (FC) and genomic data are used with a programming language interface, lacking an easy-to-navigate GUI frontend. This exacerbates two problems found in these types of data: demographic confounds and quality control in the face of high dimensionality of features. The reason is that it is too slow and cumbersome to use a programming interface to create all the necessary visualizations required to identify all correlations, confounding effects, or quality control problems in a dataset. To remedy this situation, we have developed ImageNomer, a data visualization and analysis tool that allows inspection of both subject-level and cohort-level demographic, genomic, and imaging features. The software is Python-based, runs in a self-contained Docker image, and contains a browser-based GUI frontend. We demonstrate the usefulness of ImageNomer by identifying an unexpected race confound when predicting achievement scores in the Philadelphia Neurodevelopmental Cohort (PNC) dataset. In the past, many studies have attempted to use FC to identify achievement-related features in fMRI. Using ImageNomer, we find a clear potential for confounding effects of race. Using correlation analysis in the ImageNomer software, we show that FCs correlated with Wide Range Achievement Test (WRAT) score are in fact more highly correlated with race. Investigating further, we find that whereas both FC and SNP (genomic) features can account for 10-15\% of WRAT score variation, this predictive ability disappears when controlling for race. In this work, we demonstrate the advantage of our ImageNomer GUI tool in data exploration and confound detection. Additionally, this work identifies race as a strong confound in FC data and casts doubt on the possibility of finding unbiased achievement-related features in fMRI and SNP data of healthy adolescents.

In the upcoming decade, deep learning may revolutionize the natural sciences, enhancing our capacity to model and predict natural occurrences. This could herald a new era of scientific exploration, bringing significant advancements across sectors from drug development to renewable energy. To answer this call, we present DeepSpeed4Science initiative (deepspeed4science.ai) which aims to build unique capabilities through AI system technology innovations to help domain experts to unlock today's biggest science mysteries. By leveraging DeepSpeed's current technology pillars (training, inference and compression) as base technology enablers, DeepSpeed4Science will create a new set of AI system technologies tailored for accelerating scientific discoveries by addressing their unique complexity beyond the common technical approaches used for accelerating generic large language models (LLMs). In this paper, we showcase the early progress we made with DeepSpeed4Science in addressing two of the critical system challenges in structural biology research.

Generative adversarial networks (GANs) have been extremely successful in generating samples, from seemingly high dimensional probability measures. However, these methods struggle to capture the temporal dependence of joint probability distributions induced by time-series data. Furthermore, long time-series data streams hugely increase the dimension of the target space, which may render generative modelling infeasible. To overcome these challenges, motivated by the autoregressive models in econometric, we are interested in the conditional distribution of future time series given the past information. We propose the generic conditional Sig-WGAN framework by integrating Wasserstein-GANs (WGANs) with mathematically principled and efficient path feature extraction called the signature of a path. The signature of a path is a graded sequence of statistics that provides a universal description for a stream of data, and its expected value characterises the law of the time-series model. In particular, we develop the conditional Sig-$W_1$ metric, that captures the conditional joint law of time series models, and use it as a discriminator. The signature feature space enables the explicit representation of the proposed discriminators which alleviates the need for expensive training. We validate our method on both synthetic and empirical dataset and observe that our method consistently and significantly outperforms state-of-the-art benchmarks with respect to measures of similarity and predictive ability.  ( 3 min )

In data-driven optimization, sample average approximation (SAA) is known to suffer from the so-called optimizer's curse that causes an over-optimistic evaluation of the solution performance. We argue that a special type of distributionallly robust optimization (DRO) formulation offers theoretical advantages in correcting for this optimizer's curse compared to simple ``margin'' adjustments to SAA and other DRO approaches: It attains a statistical bound on the out-of-sample performance, for a wide class of objective functions and distributions, that is nearly tightest in terms of exponential decay rate. This DRO uses an ambiguity set based on a Kullback Leibler (KL) divergence smoothed by the Wasserstein or L\'evy-Prokhorov (LP) distance via a suitable distance optimization. Computationally, we also show that such a DRO, and its generalized versions using smoothed $f$-divergence, are not harder than DRO problems based on $f$-divergence or Wasserstein distances, rendering our DRO formulations both statistically optimal and computationally viable.  ( 2 min )

What is the relationship between model architecture and the ability to perform in-context learning? In this empirical study, we take the first steps towards answering this question. In particular, we evaluate fifteen model architectures across a suite of synthetic in-context learning tasks. The selected architectures represent a broad range of paradigms, including recurrent and convolution-based neural networks, transformers, and emerging attention alternatives. We discover that all considered architectures can perform in-context learning under certain conditions. However, contemporary architectures are found to be the best performing, especially as task complexity grows. Additionally, our follow-up experiments delve into various factors that influence in-context learning. We observe varied sensitivities among architectures with respect to hyperparameter settings. Our study of training dynamics reveals that certain architectures exhibit a smooth, progressive learning trajectory, while others demonstrate periods of stagnation followed by abrupt mastery of the task. Finally, and somewhat surprisingly, we find that several emerging attention alternatives are more robust in-context learners than transformers; since such approaches have constant-sized memory footprints at inference time, this result opens the future possibility of scaling up in-context learning to vastly larger numbers of in-context examples.

This paper introduces BarlowRL, a data-efficient reinforcement learning agent that combines the Barlow Twins self-supervised learning framework with DER (Data-Efficient Rainbow) algorithm. BarlowRL outperforms both DER and its contrastive counterpart CURL on the Atari 100k benchmark. BarlowRL avoids dimensional collapse by enforcing information spread to the whole space. This helps RL algorithms to utilize uniformly spread state representation that eventually results in a remarkable performance. The integration of Barlow Twins with DER enhances data efficiency and achieves superior performance in the RL tasks. BarlowRL demonstrates the potential of incorporating self-supervised learning techniques to improve RL algorithms.

We propose a generalization of the synthetic controls and synthetic interventions methodology to incorporate network interference. We consider the estimation of unit-specific potential outcomes from panel data in the presence of spillover across units and unobserved confounding. Key to our approach is a novel latent factor model that takes into account network interference and generalizes the factor models typically used in panel data settings. We propose an estimator, Network Synthetic Interventions (NSI), and show that it consistently estimates the mean outcomes for a unit under an arbitrary set of counterfactual treatments for the network. We further establish that the estimator is asymptotically normal. We furnish two validity tests for whether the NSI estimator reliably generalizes to produce accurate counterfactual estimates. We provide a novel graph-based experiment design that guarantees the NSI estimator produces accurate counterfactual estimates, and also analyze the sample complexity of the proposed design. We conclude with simulations that corroborate our theoretical findings.

Neural-symbolic computing (NeSy), which pursues the integration of the symbolic and statistical paradigms of cognition, has been an active research area of Artificial Intelligence (AI) for many years. As NeSy shows promise of reconciling the advantages of reasoning and interpretability of symbolic representation and robust learning in neural networks, it may serve as a catalyst for the next generation of AI. In the present paper, we provide a systematic overview of the recent developments and important contributions of NeSy research. Firstly, we introduce study history of this area, covering early work and foundations. We further discuss background concepts and identify key driving factors behind the development of NeSy. Afterward, we categorize recent landmark approaches along several main characteristics that underline this research paradigm, including neural-symbolic integration, knowledge representation, knowledge embedding, and functionality. Next, we briefly discuss the successful application of modern NeSy approaches in several domains. Then, we benchmark several NeSy methods on three representative application tasks. Finally, we identify the open problems together with potential future research directions. This survey is expected to help new researchers enter this rapidly evolving field and accelerate the progress towards data-and knowledge-driven AI.  ( 2 min )

We study the problem of future step anticipation in procedural videos. Given a video of an ongoing procedural activity, we predict a plausible next procedure step described in rich natural language. While most previous work focus on the problem of data scarcity in procedural video datasets, another core challenge of future anticipation is how to account for multiple plausible future realizations in natural settings. This problem has been largely overlooked in previous work. To address this challenge, we frame future step prediction as modelling the distribution of all possible candidates for the next step. Specifically, we design a generative model that takes a series of video clips as input, and generates multiple plausible and diverse candidates (in natural language) for the next step. Following previous work, we side-step the video annotation scarcity by pretraining our model on a large text-based corpus of procedural activities, and then transfer the model to the video domain. Our experiments, both in textual and video domains, show that our model captures diversity in the next step prediction and generates multiple plausible future predictions. Moreover, our model establishes new state-of-the-art results on YouCookII, where it outperforms existing baselines on the next step anticipation. Finally, we also show that our model can successfully transfer from text to the video domain zero-shot, ie, without fine-tuning or adaptation, and produces good-quality future step predictions from video.

Graph-structured data is ubiquitous in the world which models complex relationships between objects, enabling various Web applications. Daily influxes of unlabeled graph data on the Web offer immense potential for these applications. Graph self-supervised algorithms have achieved significant success in acquiring generic knowledge from abundant unlabeled graph data. These pre-trained models can be applied to various downstream Web applications, saving training time and improving downstream (target) performance. However, different graphs, even across seemingly similar domains, can differ significantly in terms of attribute semantics, posing difficulties, if not infeasibility, for transferring the pre-trained models to downstream tasks. Concretely speaking, for example, the additional task-specific node information in downstream tasks (specificity) is usually deliberately omitted so that the pre-trained representation (transferability) can be leveraged. The trade-off as such is termed as "transferability-specificity dilemma" in this work. To address this challenge, we introduce an innovative deployment module coined as GraphControl, motivated by ControlNet, to realize better graph domain transfer learning. Specifically, by leveraging universal structural pre-trained models and GraphControl, we align the input space across various graphs and incorporate unique characteristics of target data as conditional inputs. These conditions will be progressively integrated into the model during fine-tuning or prompt tuning through ControlNet, facilitating personalized deployment. Extensive experiments show that our method significantly enhances the adaptability of pre-trained models on target attributed datasets, achieving 1.4-3x performance gain. Furthermore, it outperforms training-from-scratch methods on target data with a comparable margin and exhibits faster convergence.

Large vision-language models have achieved outstanding performance, but their size and computational requirements make their deployment on resource-constrained devices and time-sensitive tasks impractical. Model distillation, the process of creating smaller, faster models that maintain the performance of larger models, is a promising direction towards the solution. This paper investigates the distillation of visual representations in large teacher vision-language models into lightweight student models using a small- or mid-scale dataset. Notably, this study focuses on open-vocabulary out-of-distribution (OOD) generalization, a challenging problem that has been overlooked in previous model distillation literature. We propose two principles from vision and language modality perspectives to enhance student's OOD generalization: (1) by better imitating teacher's visual representation space, and carefully promoting better coherence in vision-language alignment with the teacher; (2) by enriching the teacher's language representations with informative and finegrained semantic attributes to effectively distinguish between different labels. We propose several metrics and conduct extensive experiments to investigate their techniques. The results demonstrate significant improvements in zero-shot and few-shot student performance on open-vocabulary out-of-distribution classification, highlighting the effectiveness of our proposed approaches. Poster: https://xuanlinli17.github.io/pdfs/iccv23_large_vlm_distillation_poster.pdf Code: https://github.com/xuanlinli17/large_vlm_distillation_ood  ( 2 min )

Humans often acquire new skills through observation and imitation. For robotic agents, learning from the plethora of unlabeled video demonstration data available on the Internet necessitates imitating the expert without access to its action, presenting a challenge known as Imitation Learning from Observations (ILfO). A common approach to tackle ILfO problems is to convert them into inverse reinforcement learning problems, utilizing a proxy reward computed from the agent's and the expert's observations. Nonetheless, we identify that tasks characterized by a progress dependency property pose significant challenges for such approaches; in these tasks, the agent needs to initially learn the expert's preceding behaviors before mastering the subsequent ones. Our investigation reveals that the main cause is that the reward signals assigned to later steps hinder the learning of initial behaviors. To address this challenge, we present a novel ILfO framework that enables the agent to master earlier behaviors before advancing to later ones. We introduce an Automatic Discount Scheduling (ADS) mechanism that adaptively alters the discount factor in reinforcement learning during the training phase, prioritizing earlier rewards initially and gradually engaging later rewards only when the earlier behaviors have been mastered. Our experiments, conducted on nine Meta-World tasks, demonstrate that our method significantly outperforms state-of-the-art methods across all tasks, including those that are unsolvable by them.

Speech conveys more information than text, as the same word can be uttered in various voices to convey diverse information. Compared to traditional text-to-speech (TTS) methods relying on speech prompts (reference speech) for voice variability, using text prompts (descriptions) is more user-friendly since speech prompts can be hard to find or may not exist at all. TTS approaches based on the text prompt face two main challenges: 1) the one-to-many problem, where not all details about voice variability can be described in the text prompt, and 2) the limited availability of text prompt datasets, where vendors and large cost of data labeling are required to write text prompts for speech. In this work, we introduce PromptTTS 2 to address these challenges with a variation network to provide variability information of voice not captured by text prompts, and a prompt generation pipeline to utilize the large language models (LLM) to compose high quality text prompts. Specifically, the variation network predicts the representation extracted from the reference speech (which contains full information about voice variability) based on the text prompt representation. For the prompt generation pipeline, it generates text prompts for speech with a speech language understanding model to recognize voice attributes (e.g., gender, speed) from speech and a large language model to formulate text prompts based on the recognition results. Experiments on a large-scale (44K hours) speech dataset demonstrate that compared to the previous works, PromptTTS 2 generates voices more consistent with text prompts and supports the sampling of diverse voice variability, thereby offering users more choices on voice generation. Additionally, the prompt generation pipeline produces high-quality text prompts, eliminating the large labeling cost. The demo page of PromptTTS 2 is available online.

We introduce a neural-preconditioned iterative solver for Poisson equations with mixed boundary conditions. The Poisson equation is ubiquitous in scientific computing: it governs a wide array of physical phenomena, arises as a subproblem in many numerical algorithms, and serves as a model problem for the broader class of elliptic PDEs. The most popular Poisson discretizations yield large sparse linear systems. At high resolution, and for performance-critical applications, iterative solvers can be advantageous for these -- but only when paired with powerful preconditioners. The core of our solver is a neural network trained to approximate the inverse of a discrete structured-grid Laplace operator for a domain of arbitrary shape and with mixed boundary conditions. The structure of this problem motivates a novel network architecture that we demonstrate is highly effective as a preconditioner even for boundary conditions outside the training set. We show that on challenging test cases arising from an incompressible fluid simulation, our method outperforms state-of-the-art solvers like algebraic multigrid as well as some recent neural preconditioners.

The past decade has witnessed significant advances in time series modeling with deep learning. While achieving state-of-the-art results, the best-performing architectures vary highly across applications and domains. Meanwhile, for natural language processing, the Generative Pre-trained Transformer (GPT) has demonstrated impressive performance via training one general-purpose model across various textual datasets. It is intriguing to explore whether GPT-type architectures can be effective for time series, capturing the intrinsic dynamic attributes and leading to significant accuracy improvements. In this paper, we propose a novel framework, TEMPO, that can effectively learn time series representations. We focus on utilizing two essential inductive biases of the time series task for pre-trained models: (i) decomposition of the complex interaction between trend, seasonal and residual components; and (ii) introducing the selection-based prompts to facilitate distribution adaptation in non-stationary time series. TEMPO expands the capability for dynamically modeling real-world temporal phenomena from data within diverse domains. Our experiments demonstrate the superior performance of TEMPO over state-of-the-art methods on a number of time series benchmark datasets. This performance gain is observed not only in standard supervised learning settings but also in scenarios involving previously unseen datasets as well as in scenarios with multi-modal inputs. This compelling finding highlights TEMPO's potential to constitute a foundational model-building framework.

Hierarchical time series are common in several applied fields. The forecasts for these time series are required to be coherent, that is, to satisfy the constraints given by the hierarchy. The most popular technique to enforce coherence is called reconciliation, which adjusts the base forecasts computed for each time series. However, recent works on probabilistic reconciliation present several limitations. In this paper, we propose a new approach based on conditioning to reconcile any type of forecast distribution. We then introduce a new algorithm, called Bottom-Up Importance Sampling, to efficiently sample from the reconciled distribution. It can be used for any base forecast distribution: discrete, continuous, or in the form of samples, providing a major speedup compared to the current methods. Experiments on several temporal hierarchies show a significant improvement over base probabilistic forecasts.  ( 2 min )

Lion (Evolved Sign Momentum), a new optimizer discovered through program search, has shown promising results in training large AI models. It performs comparably or favorably to AdamW but with greater memory efficiency. As we can expect from the results of a random search program, Lion incorporates elements from several existing algorithms, including signed momentum, decoupled weight decay, Polak, and Nesterov momentum, but does not fit into any existing category of theoretically grounded optimizers. Thus, even though Lion appears to perform well as a general-purpose optimizer for a wide range of tasks, its theoretical basis remains uncertain. This lack of theoretical clarity limits opportunities to further enhance and expand Lion's efficacy. This work aims to demystify Lion. Based on both continuous-time and discrete-time analysis, we demonstrate that Lion is a theoretically novel and principled approach for minimizing a general loss function $f(x)$ while enforcing a bound constraint $\|x\|_\infty \leq 1/\lambda$. Lion achieves this through the incorporation of decoupled weight decay, where $\lambda$ represents the weight decay coefficient. Our analysis is made possible by the development of a new Lyapunov function for the Lion updates. It applies to a broader family of Lion-$\kappa$ algorithms, where the $\text{sign}(\cdot)$ operator in Lion is replaced by the subgradient of a convex function $\kappa$, leading to the solution of a general composite optimization problem of $\min_x f(x) + \kappa^*(x)$. Our findings provide valuable insights into the dynamics of Lion and pave the way for further improvements and extensions of Lion-related algorithms.

Matrix-variate distributions are a recent addition to the model-based clustering field, thereby making it possible to analyze data in matrix form with complex structure such as images and time series. Due to its recent appearance, there is limited literature on matrix-variate data, with even less on dealing with outliers in these models. An approach for clustering matrix-variate normal data with outliers is discussed. The approach, which uses the distribution of subset log-likelihoods, extends the OCLUST algorithm to matrix-variate normal data and uses an iterative approach to detect and trim outliers.

Federated Learning (FL) has surged in prominence due to its capability of collaborative model training without direct data sharing. However, the vast disparity in local data distributions among clients, often termed the non-Independent Identically Distributed (non-IID) challenge, poses a significant hurdle to FL's generalization efficacy. The scenario becomes even more complex when not all clients participate in the training process, a common occurrence due to unstable network connections or limited computational capacities. This can greatly complicate the assessment of the trained models' generalization abilities. While a plethora of recent studies has centered on the generalization gap pertaining to unseen data from participating clients with diverse distributions, the divergence between the training distributions of participating clients and the testing distributions of non-participating ones has been largely overlooked. In response, our paper unveils an information-theoretic generalization framework for FL. Specifically, it quantifies generalization errors by evaluating the information entropy of local distributions and discerning discrepancies across these distributions. Inspired by our deduced generalization bounds, we introduce a weighted aggregation approach and a duo of client selection strategies. These innovations aim to bolster FL's generalization prowess by encompassing a more varied set of client data distributions. Our extensive empirical evaluations reaffirm the potency of our proposed methods, aligning seamlessly with our theoretical construct.

Spiking neural networks (SNNs) have demonstrated the capability to achieve comparable performance to deep neural networks (DNNs) in both visual and linguistic domains while offering the advantages of improved energy efficiency and adherence to biological plausibility. However, the extension of such single-modality SNNs into the realm of multimodal scenarios remains an unexplored territory. Drawing inspiration from the concept of contrastive language-image pre-training (CLIP), we introduce a novel framework, named SpikeCLIP, to address the gap between two modalities within the context of spike-based computing through a two-step recipe involving ``Alignment Pre-training + Dual-Loss Fine-tuning". Extensive experiments demonstrate that SNNs achieve comparable results to their DNN counterparts while significantly reducing energy consumption across a variety of datasets commonly used for multimodal model evaluation. Furthermore, SpikeCLIP maintains robust performance in image classification tasks that involve class labels not predefined within specific categories.

We present the Grasp Proposal Network (GP-net), a Convolutional Neural Network model which can generate 6-DoF grasps from flexible viewpoints, e.g. as experienced by mobile manipulators. To train GP-net, we synthetically generate a dataset containing depth-images and ground-truth grasp information. In real-world experiments, we use the EGAD evaluation benchmark to evaluate GP-net against two commonly used algorithms, the Volumetric Grasping Network (VGN) and the Grasp Pose Detection package (GPD), on a PAL TIAGo mobile manipulator. In contrast to the state-of-the-art methods in robotic grasping, GP-net can be used for grasping objects from flexible, unknown viewpoints without the need to define the workspace and achieves a grasp success of 54.4% compared to 51.6% for VGN and 44.2% for GPD. We provide a ROS package along with our code and pre-trained models at https://aucoroboticsmu.github.io/GP-net/.

Federated learning (FL) is a decentralized machine learning approach where independent learners process data privately. Its goal is to create a robust and accurate model by aggregating and retraining local models over multiple rounds. However, FL faces challenges regarding data heterogeneity and model aggregation effectiveness. In order to simulate real-world data, researchers use methods for data partitioning that transform a dataset designated for centralized learning into a group of sub-datasets suitable for distributed machine learning with different data heterogeneity. In this paper, we study the currently popular data partitioning techniques and visualize their main disadvantages: the lack of precision in the data diversity, which leads to unreliable heterogeneity indexes, and the inability to incrementally challenge the FL algorithms. To resolve this problem, we propose a method that leverages entropy and symmetry to construct 'the most challenging' and controllable data distributions with gradual difficulty. We introduce a metric to measure data heterogeneity among the learning agents and a transformation technique that divides any dataset into splits with precise data diversity. Through a comparative study, we demonstrate the superiority of our method over existing FL data partitioning approaches, showcasing its potential to challenge model aggregation algorithms. Experimental results indicate that our approach gradually challenges the FL strategies, and the models trained on FedSym distributions are more distinct.

Large language models (LLMs) have demonstrated remarkable capabilities in natural language understanding across various domains, including healthcare and finance. For some tasks, LLMs achieve similar or better performance than trained human beings, therefore it is reasonable to employ human exams (e.g., certification tests) to assess the performance of LLMs. We present a comprehensive evaluation of popular LLMs, such as Llama 2 and GPT, on their ability to answer agriculture-related questions. In our evaluation, we also employ RAG (Retrieval-Augmented Generation) and ER (Ensemble Refinement) techniques, which combine information retrieval, generation capabilities, and prompting strategies to improve the LLMs' performance. To demonstrate the capabilities of LLMs, we selected agriculture exams and benchmark datasets from three of the largest agriculture producer countries: Brazil, India, and the USA. Our analysis highlights GPT-4's ability to achieve a passing score on exams to earn credits for renewing agronomist certifications, answering 93% of the questions correctly and outperforming earlier general-purpose models, which achieved 88% accuracy. On one of our experiments, GPT-4 obtained the highest performance when compared to human subjects. This performance suggests that GPT-4 could potentially pass on major graduate education admission tests or even earn credits for renewing agronomy certificates. We also explore the models' capacity to address general agriculture-related questions and generate crop management guidelines for Brazilian and Indian farmers, utilizing robust datasets from the Brazilian Agency of Agriculture (Embrapa) and graduate program exams from India. The results suggest that GPT-4, ER, and RAG can contribute meaningfully to agricultural education, assessment, and crop management practice, offering valuable insights to farmers and agricultural professionals.

Detecting out-of-distribution (OOD) data is a critical challenge in machine learning due to model overconfidence, often without awareness of their epistemological limits. We hypothesize that ``neural collapse'', a phenomenon affecting in-distribution data for models trained beyond loss convergence, also influences OOD data. To benefit from this interplay, we introduce NECO, a novel post-hoc method for OOD detection, which leverages the geometric properties of ``neural collapse'' and of principal component spaces to identify OOD data. Our extensive experiments demonstrate that NECO achieves state-of-the-art results on both small and large-scale OOD detection tasks while exhibiting strong generalization capabilities across different network architectures. Furthermore, we provide a theoretical explanation for the effectiveness of our method in OOD detection. We plan to release the code after the anonymity period.

Generalizable implicit neural representation (INR) enables a single continuous function, i.e., a coordinate-based neural network, to represent multiple data instances by modulating its weights or intermediate features using latent codes. However, the expressive power of the state-of-the-art modulation is limited due to its inability to localize and capture fine-grained details of data entities such as specific pixels and rays. To address this issue, we propose a novel framework for generalizable INR that combines a transformer encoder with a locality-aware INR decoder. The transformer encoder predicts a set of latent tokens from a data instance to encode local information into each latent token. The locality-aware INR decoder extracts a modulation vector by selectively aggregating the latent tokens via cross-attention for a coordinate input and then predicts the output by progressively decoding with coarse-to-fine modulation through multiple frequency bandwidths. The selective token aggregation and the multi-band feature modulation enable us to learn locality-aware representation in spatial and spectral aspects, respectively. Our framework significantly outperforms previous generalizable INRs and validates the usefulness of the locality-aware latents for downstream tasks such as image generation.

The proliferation of large AI models trained on uncurated, often sensitive web-scraped data has raised significant privacy concerns. One of the concerns is that adversaries can extract information about the training data using privacy attacks. Unfortunately, the task of removing specific information from the models without sacrificing performance is not straightforward and has proven to be challenging. We propose a rather easy yet effective defense based on backdoor attacks to remove private information such as names of individuals from models, and focus in this work on text encoders. Specifically, through strategic insertion of backdoors, we align the embeddings of sensitive phrases with those of neutral terms-"a person" instead of the person's name. Our empirical results demonstrate the effectiveness of our backdoor-based defense on CLIP by assessing its performance using a specialized privacy attack for zero-shot classifiers. Our approach provides not only a new "dual-use" perspective on backdoor attacks, but also presents a promising avenue to enhance the privacy of individuals within models trained on uncurated web-scraped data.

Sparse logistic regression is for classification and feature selection simultaneously. Although many studies have been done to solve $\ell_1$-regularized logistic regression, there is no equivalently abundant work on solving sparse logistic regression with nonconvex regularization term. In this paper, we propose a unified framework to solve $\ell_1$-regularized logistic regression, which can be naturally extended to nonconvex regularization term, as long as certain requirement is satisfied. In addition, we also utilize a different line search criteria to guarantee monotone convergence for various regularization terms. Empirical experiments on binary classification tasks with real-world datasets demonstrate our proposed algorithms are capable of performing classification and feature selection effectively at a lower computational cost.

Recently, contrastive learning has become a key component in fine-tuning code search models for software development efficiency and effectiveness. It pulls together positive code snippets while pushing negative samples away given search queries. Among contrastive learning, InfoNCE is the most widely used loss function due to its better performance. However, the following problems in negative samples of InfoNCE may deteriorate its representation learning: 1) The existence of false negative samples in large code corpora due to duplications. 2). The failure to explicitly differentiate between the potential relevance of negative samples. As an example, a bubble sorting algorithm example is less ``negative'' than a file saving function for the quick sorting algorithm query. In this paper, we tackle the above problems by proposing a simple yet effective Soft-InfoNCE loss that inserts weight terms into InfoNCE. In our proposed loss function, we apply three methods to estimate the weights of negative pairs and show that the vanilla InfoNCE loss is a special case of Soft-InfoNCE. Theoretically, we analyze the effects of Soft-InfoNCE on controlling the distribution of learnt code representations and on deducing a more precise mutual information estimation. We furthermore discuss the superiority of proposed loss functions with other design alternatives. Extensive experiments demonstrate the effectiveness of Soft-InfoNCE and weights estimation methods under state-of-the-art code search models on a large-scale public dataset consisting of six programming languages. Source code is available at \url{https://github.com/Alex-HaochenLi/Soft-InfoNCE}.

In modern communication systems, efficient and reliable information dissemination is crucial for supporting critical operations across domains like disaster response, autonomous vehicles, and sensor networks. This paper introduces a Multi-Agent Reinforcement Learning (MARL) approach as a significant step forward in achieving more decentralized, efficient, and collaborative solutions. We propose a Partially Observable Stochastic Game (POSG) formulation for information dissemination empowering each agent to decide on message forwarding independently, based on their one-hop neighborhood. This constitutes a significant paradigm shift from traditional heuristics based on Multi-Point Relay (MPR) selection. Our approach harnesses Graph Convolutional Reinforcement Learning, employing Graph Attention Networks (GAT) with dynamic attention to capture essential network features. We propose two approaches, L-DGN and HL-DGN, which differ in the information that is exchanged among agents. We evaluate the performance of our decentralized approaches, by comparing them with a widely-used MPR heuristic, and we show that our trained policies are able to efficiently cover the network while bypassing the MPR set selection process. Our approach is a first step toward supporting the resilience of real-world broadcast communication infrastructures via learned, collaborative information dissemination.

Proximal gradient-based optimization is one of the most common strategies to solve inverse problem of images, and it is easy to implement. However, these techniques often generate heavy artifacts in image reconstruction. One of the most popular refinement methods is to fine-tune the regularization parameter to alleviate such artifacts, but it may not always be sufficient or applicable due to increased computational costs. In this work, we propose a deep geometric incremental learning framework based on the second Nesterov proximal gradient optimization. The proposed end-to-end network not only has the powerful learning ability for high-/low-frequency image features, but also can theoretically guarantee that geometric texture details will be reconstructed from preliminary linear reconstruction. Furthermore, it can avoid the risk of intermediate reconstruction results falling outside the geometric decomposition domains and achieve fast convergence. Our reconstruction framework is decomposed into four modules including general linear reconstruction, cascade geometric incremental restoration, Nesterov acceleration, and post-processing. In the image restoration step, a cascade geometric incremental learning module is designed to compensate for missing texture information from different geometric spectral decomposition domains. Inspired by the overlap-tile strategy, we also develop a post-processing module to remove the block effect in patch-wise-based natural image reconstruction. All parameters in the proposed model are learnable, an adaptive initialization technique of physical parameters is also employed to make model flexibility and ensure converging smoothly. We compare the reconstruction performance of the proposed method with existing state-of-the-art methods to demonstrate its superiority. Our source codes are available at https://github.com/fanxiaohong/Nest-DGIL.

The accurate and efficient diagnosis of COVID-19 is of paramount importance, particularly in the context of large-scale medical imaging datasets. In this preprint paper, we propose a novel approach for COVID-19 diagnosis using CT images that leverages the power of Swin Transformer models, state-of-the-art solutions in computer vision tasks. Our method includes a systematic approach for patient-level predictions, where individual CT slices are classified as COVID-19 or non-COVID, and the patient's overall diagnosis is determined through majority voting. The application of the Swin Transformer in this context results in patient-level predictions that demonstrate exceptional diagnostic accuracy. In terms of evaluation metrics, our approach consistently outperforms the baseline, as well as numerous competing methods, showcasing its effectiveness in COVID-19 diagnosis. The macro F1 score achieved by our model exceeds the baseline and offers a robust solution for accurate diagnosis.

Foundation models (FM) have demonstrated remarkable performance across a wide range of tasks (especially in the fields of natural language processing and computer vision), primarily attributed to their ability to comprehend instructions and access extensive, high-quality data. This not only showcases their current effectiveness but also sets a promising trajectory towards the development of artificial general intelligence. Unfortunately, due to multiple constraints, the raw data of the model used for large model training are often inaccessible, so the use of end-to-end models for downstream tasks has become a new research trend, which we call Learn From Model (LFM) in this article. LFM focuses on the research, modification, and design of FM based on the model interface, so as to better understand the model structure and weights (in a black box environment), and to generalize the model to downstream tasks. The study of LFM techniques can be broadly categorized into five major areas: model tuning, model distillation, model reuse, meta learning and model editing. Each category encompasses a repertoire of methods and strategies that aim to enhance the capabilities and performance of FM. This paper gives a comprehensive review of the current methods based on FM from the perspective of LFM, in order to help readers better understand the current research status and ideas. To conclude, we summarize the survey by highlighting several critical areas for future exploration and addressing open issues that require further attention from the research community. The relevant papers we investigated in this article can be accessed at .

We observe the emergence of binary encoding within the latent space of deep-neural-network classifiers. Such binary encoding is induced by introducing a linear penultimate layer, which is equipped during training with a loss function that grows as $\exp(\vec{x}^2)$, where $\vec{x}$ are the coordinates in the latent space. The phenomenon we describe represents a specific instance of a well-documented occurrence known as \textit{neural collapse}, which arises in the terminal phase of training and entails the collapse of latent class means to the vertices of a simplex equiangular tight frame (ETF). We show that binary encoding accelerates convergence toward the simplex ETF and enhances classification accuracy.

We refer to the setting where the (partial) derivatives of a neural network's (NN's) predictions with respect to its inputs are used as additional training signal as a derivative-constrained (DC) NN. This situation is common in physics-informed settings in the natural sciences. We propose an integrated RELU (IReLU) activation function to improve training of DC NNs. We also investigate denormalization and label rescaling to help stabilize DC training. We evaluate our methods on physics-informed settings including quantum chemistry and Scientific Machine Learning (SciML) tasks. We demonstrate that existing architectures with IReLU activations combined with denormalization and label rescaling better incorporate training signal provided by derivative constraints.

Neural ordinary differential equations (neural ODEs) are a popular family of continuous-depth deep learning models. In this work, we consider a large family of parameterized ODEs with continuous-in-time parameters, which include time-dependent neural ODEs. We derive a generalization bound for this class by a Lipschitz-based argument. By leveraging the analogy between neural ODEs and deep residual networks, our approach yields in particular a generalization bound for a class of deep residual networks. The bound involves the magnitude of the difference between successive weight matrices. We illustrate numerically how this quantity affects the generalization capability of neural networks.  ( 2 min )

Many evolutionary algorithms (EAs) take advantage of parallel evaluation of candidates. However, if evaluation times vary significantly, many worker nodes (i.e.,\ compute clients) are idle much of the time, waiting for the next generation to be created. Evolutionary neural architecture search (ENAS), a class of EAs that optimizes the architecture and hyperparameters of deep neural networks, is particularly vulnerable to this issue. This paper proposes a generic asynchronous evaluation strategy (AES) that is then adapted to work with ENAS. AES increases throughput by maintaining a queue of up to $K$ individuals ready to be sent to the workers for evaluation and proceeding to the next generation as soon as $M<<K$ individuals have been evaluated. A suitable value for $M$ is determined experimentally, balancing diversity and efficiency. To showcase the generality and power of AES, it was first evaluated in eight-line sorting network design (a single-population optimization task with limited evaluation-time variability), achieving an over two-fold speedup. Next, it was evaluated in 11-bit multiplexer design (a single-population discovery task with extended variability), where a 14-fold speedup was observed. It was then scaled up to ENAS for image captioning (a multi-population open-ended-optimization task), resulting in an over two-fold speedup. In all problems, a multifold performance improvement was observed, suggesting that AES is a promising method for parallelizing the evolution of complex systems with long and variable evaluation times, such as those in ENAS.

Recent works have proposed that activations in language models can be modelled as sparse linear combinations of vectors corresponding to features of input text. Under this assumption, these works aimed to reconstruct feature directions using sparse coding. We develop metrics to assess the success of these sparse coding techniques and test the validity of the linearity and sparsity assumptions. We show our metrics can predict the level of sparsity on synthetic sparse linear activations, and can distinguish between sparse linear data and several other distributions. We use our metrics to measure levels of sparsity in several language models. We find evidence that language model activations can be accurately modelled by sparse linear combinations of features, significantly more so than control datasets. We also show that model activations appear to be sparsest in the first and final layers.

Prompt tuning (PT), where a small amount of trainable soft (continuous) prompt vectors is affixed to the input of language models (LM), has shown promising results across various tasks and models for parameter-efficient fine-tuning (PEFT). PT stands out from other PEFT approaches because it maintains competitive performance with fewer trainable parameters and does not drastically scale up its parameters as the model size expands. However, PT introduces additional soft prompt tokens, leading to longer input sequences, which significantly impacts training and inference time and memory usage due to the Transformer's quadratic complexity. Particularly concerning for Large Language Models (LLMs) that face heavy daily querying. To address this issue, we propose Decomposed Prompt Tuning (DePT), which decomposes the soft prompt into a shorter soft prompt and a pair of low-rank matrices that are then optimised with two different learning rates. This allows DePT to achieve better performance while saving over 20% memory and time costs compared to vanilla PT and its variants, without changing trainable parameter sizes. Through extensive experiments on 23 natural language processing (NLP) and vision-language (VL) tasks, we demonstrate that DePT outperforms state-of-the-art PEFT approaches, including the full fine-tuning baseline in some scenarios. Additionally, we empirically show that DEPT grows more efficient as the model size increases. Our further study reveals that DePT integrates seamlessly with parameter-efficient transfer learning in the few-shot learning setting and highlights its adaptability to various model architectures and sizes.

In practice, deep neural networks are often able to easily interpolate their training data. To understand this phenomenon, many works have aimed to quantify the memorization capacity of a neural network architecture: the largest number of points such that the architecture can interpolate any placement of these points with any assignment of labels. For real-world data, however, one intuitively expects the presence of a benign structure so that interpolation already occurs at a smaller network size than suggested by memorization capacity. In this paper, we investigate interpolation by adopting an instance-specific viewpoint. We introduce a simple randomized algorithm that, given a fixed finite dataset with two classes, with high probability constructs an interpolating three-layer neural network in polynomial time. The required number of parameters is linked to geometric properties of the two classes and their mutual arrangement. As a result, we obtain guarantees that are independent of the number of samples and hence move beyond worst-case memorization capacity bounds. We illustrate the effectiveness of the algorithm in non-pathological situations with extensive numerical experiments and link the insights back to the theoretical results.

A recent empirical observation (Li et al., 2022b) of activation sparsity in MLP blocks offers an opportunity to drastically reduce computation costs for free. Although having attributed it to training dynamics, existing theoretical explanations of activation sparsity are restricted to shallow networks, small training steps and special training, despite its emergence in deep models standardly trained for a large number of steps. To fill these gaps, we propose the notion of gradient sparsity as one source of activation sparsity and a theoretical explanation based on it that sees sparsity a necessary step to adversarial robustness w.r.t. hidden features and parameters, which is approximately the flatness of minima for well-learned models. The theory applies to standardly trained LayerNorm-ed MLPs, and further to Transformers or other architectures trained with weight noises. Eliminating other sources of flatness except for sparsity, we discover the phenomenon that the ratio between the largest and smallest non-zero singular values of weight matrices is small. When discussing the emergence of this spectral concentration, we use random matrix theory (RMT) as a powerful tool to analyze stochastic gradient noises. Validational experiments are conducted to verify our gradient-sparsity-based explanation. We propose two plug-and-play modules for both training and finetuning for sparsity. Experiments on ImageNet-1k and C4 demonstrate their 50% sparsity improvements, indicating further potential cost reduction in both training and inference.

This letter proposes a novel relaying framework, semantic-forward (SF), for cooperative communications towards the sixth-generation (6G) wireless networks. The SF relay extracts and transmits the semantic features, which reduces forwarding payload, and also improves the network robustness against intra-link errors. Based on the theoretical basis for cooperative communications with side information and the turbo principle, we design a joint source-channel coding algorithm to iteratively exchange the extrinsic information for enhancing the decoding gains at the destination. Surprisingly, simulation results indicate that even in bad channel conditions, SF relaying can still effectively improve the recovered information quality.

Although it has been demonstrated that Natural Language Processing (NLP) algorithms are vulnerable to deliberate attacks, the question of whether such weaknesses can lead to software security threats is under-explored. To bridge this gap, we conducted vulnerability tests on Text-to-SQL systems that are commonly used to create natural language interfaces to databases. We showed that the Text-to-SQL modules within six commercial applications can be manipulated to produce malicious code, potentially leading to data breaches and Denial of Service attacks. This is the first demonstration that NLP models can be exploited as attack vectors in the wild. In addition, experiments using four open-source language models verified that straightforward backdoor attacks on Text-to-SQL systems achieve a 100% success rate without affecting their performance. The aim of this work is to draw the community's attention to potential software security issues associated with NLP algorithms and encourage exploration of methods to mitigate against them.

Literature-Based Discovery (LBD) aims to discover new scientific knowledge by mining papers and generating hypotheses. Standard LBD is limited to predicting pairwise relations between discrete concepts (e.g., drug-disease links), and ignores critical contexts like experimental settings (e.g., a specific patient population where a drug is evaluated) and background motivations (e.g., to find drugs without specific side effects). We address these limitations with a novel formulation of contextualized-LBD (C-LBD): generating scientific hypotheses in natural language, while grounding them in a context that controls the hypothesis search space. We present a modeling framework using retrieval of ``inspirations'' from past scientific papers. Our evaluations reveal that GPT-4 tends to generate ideas with overall low technical depth and novelty, while our inspiration prompting approaches partially mitigate this issue. Our work represents a first step toward building language models that generate new ideas derived from scientific literature.  ( 2 min )

This work identifies a simple pre-training mechanism that leads to representations exhibiting better continual and transfer learning. This mechanism -- the repeated resetting of weights in the last layer, which we nickname "zapping" -- was originally designed for a meta-continual-learning procedure, yet we show it is surprisingly applicable in many settings beyond both meta-learning and continual learning. In our experiments, we wish to transfer a pre-trained image classifier to a new set of classes, in a few shots. We show that our zapping procedure results in improved transfer accuracy and/or more rapid adaptation in both standard fine-tuning and continual learning settings, while being simple to implement and computationally efficient. In many cases, we achieve performance on par with state of the art meta-learning without needing the expensive higher-order gradients, by using a combination of zapping and sequential learning. An intuitive explanation for the effectiveness of this zapping procedure is that representations trained with repeated zapping learn features that are capable of rapidly adapting to newly initialized classifiers. Such an approach may be considered a computationally cheaper type of, or alternative to, meta-learning rapidly adaptable features with higher-order gradients. This adds to recent work on the usefulness of resetting neural network parameters during training, and invites further investigation of this mechanism.

We propose to replace vector quantization (VQ) in the latent representation of VQ-VAEs with a simple scheme termed finite scalar quantization (FSQ), where we project the VAE representation down to a few dimensions (typically less than 10). Each dimension is quantized to a small set of fixed values, leading to an (implicit) codebook given by the product of these sets. By appropriately choosing the number of dimensions and values each dimension can take, we obtain the same codebook size as in VQ. On top of such discrete representations, we can train the same models that have been trained on VQ-VAE representations. For example, autoregressive and masked transformer models for image generation, multimodal generation, and dense prediction computer vision tasks. Concretely, we employ FSQ with MaskGIT for image generation, and with UViM for depth estimation, colorization, and panoptic segmentation. Despite the much simpler design of FSQ, we obtain competitive performance in all these tasks. We emphasize that FSQ does not suffer from codebook collapse and does not need the complex machinery employed in VQ (commitment losses, codebook reseeding, code splitting, entropy penalties, etc.) to learn expressive discrete representations.

Diffusion models have shown remarkable performance on many generative tasks. Despite recent success, most diffusion models are restricted in that they only allow linear transformation of the data distribution. In contrast, broader family of transformations can potentially help train generative distributions more efficiently, simplifying the reverse process and closing the gap between the true negative log-likelihood and the variational approximation. In this paper, we present Neural Diffusion Models (NDMs), a generalization of conventional diffusion models that enables defining and learning time-dependent non-linear transformations of data. We show how to optimise NDMs using a variational bound in a simulation-free setting. Moreover, we derive a time-continuous formulation of NDMs, which allows fast and reliable inference using off-the-shelf numerical ODE and SDE solvers. Finally, we demonstrate the utility of NDMs with learnable transformations through experiments on standard image generation benchmarks, including CIFAR-10, downsampled versions of ImageNet and CelebA-HQ. NDMs outperform conventional diffusion models in terms of likelihood and produce high-quality samples.

Counterfactual Regret Minimization (CFR) and its variants are the best algorithms so far for solving large-scale incomplete information games. However, we believe that there are two problems with CFR: First, matrix multiplication is required in CFR iteration, and the time complexity of one iteration is too high; Secondly, the game characteristics in the real world are different. Just using one CFR algorithm will not be perfectly suitable for all game problems. For these two problems, this paper proposes a new algorithm called Pure CFR (PCFR) based on CFR. PCFR can be seen as a combination of CFR and Fictitious Play (FP), inheriting the concept of counterfactual regret (value) from CFR, and using the best response strategy instead of the regret matching strategy for the next iteration. This algorithm has three advantages. First, PCFR can be combined with any CFR variant. The resulting Pure MCCFR (PMCCFR) can significantly reduce the time and space complexity of one iteration. Secondly, our experiments show that the convergence speed of the PMCCFR is 2$\sim$3 times that of the MCCFR. Finally, there is a type of game that is very suitable for PCFR, we call this type of game clear-game, which is characterized by a high proportion of dominated strategies. Experiments show that in clear-game, the convergence rate of PMCCFR is two orders of magnitude higher than that of MCCFR.

Federated Learning (FL) methods adopt efficient communication technologies to distribute machine learning tasks across edge devices, reducing the overhead in terms of data storage and computational complexity compared to centralized solutions. Rather than moving large data volumes from producers (sensors, machines) to energy-hungry data centers, raising environmental concerns due to resource demands, FL provides an alternative solution to mitigate the energy demands of several learning tasks while enabling new Artificial Intelligence of Things (AIoT) applications. This paper proposes a framework for real-time monitoring of the energy and carbon footprint impacts of FL systems. The carbon tracking tool is evaluated for consensus (fully decentralized) and classical FL policies. For the first time, we present a quantitative evaluation of different computationally and communication efficient FL methods from the perspectives of energy consumption and carbon equivalent emissions, suggesting also general guidelines for energy-efficient design. Results indicate that consensus-driven FL implementations should be preferred for limiting carbon emissions when the energy efficiency of the communication is low (i.e., < 25 Kbit/Joule). Besides, quantization and sparsification operations are shown to strike a balance between learning performances and energy consumption, leading to sustainable FL designs.

The rapid increase in the number of cyber-attacks in recent years raises the need for principled methods for defending networks against malicious actors. Deep reinforcement learning (DRL) has emerged as a promising approach for mitigating these attacks. However, while DRL has shown much potential for cyber-defence, numerous challenges must be overcome before DRL can be applied to autonomous cyber-operations (ACO) at scale. Principled methods are required for environments that confront learners with very high-dimensional state spaces, large multi-discrete action spaces, and adversarial learning. Recent works have reported success in solving these problems individually. There have also been impressive engineering efforts towards solving all three for real-time strategy games. However, applying DRL to the full ACO problem remains an open challenge. Here, we survey the relevant DRL literature and conceptualize an idealised ACO-DRL agent. We provide: i.) A summary of the domain properties that define the ACO problem; ii.) A comprehensive evaluation of the extent to which domains used for benchmarking DRL approaches are comparable to ACO; iii.) An overview of state-of-the-art approaches for scaling DRL to domains that confront learners with the curse of dimensionality, and; iv.) A survey and critique of current methods for limiting the exploitability of agents within adversarial settings from the perspective of ACO. We conclude with open research questions that we hope will motivate future directions for researchers and practitioners working on ACO.

We characterize the statistical efficiency of knowledge transfer through $n$ samples from a teacher to a probabilistic student classifier with input space $\mathcal S$ over labels $\mathcal A$. We show that privileged information at three progressive levels accelerates the transfer. At the first level, only samples with hard labels are known, via which the maximum likelihood estimator attains the minimax rate $\sqrt{{|{\mathcal S}||{\mathcal A}|}/{n}}$. The second level has the teacher probabilities of sampled labels available in addition, which turns out to boost the convergence rate lower bound to ${{|{\mathcal S}||{\mathcal A}|}/{n}}$. However, under this second data acquisition protocol, minimizing a naive adaptation of the cross-entropy loss results in an asymptotically biased student. We overcome this limitation and achieve the fundamental limit by using a novel empirical variant of the squared error logit loss. The third level further equips the student with the soft labels (complete logits) on ${\mathcal A}$ given every sampled input, thereby provably enables the student to enjoy a rate ${|{\mathcal S}|}/{n}$ free of $|{\mathcal A}|$. We find any Kullback-Leibler divergence minimizer to be optimal in the last case. Numerical simulations distinguish the four learners and corroborate our theory.

Discovering achievements with a hierarchical structure in procedurally generated environments presents a significant challenge. This requires an agent to possess a broad range of abilities, including generalization and long-term reasoning. Many prior methods have been built upon model-based or hierarchical approaches, with the belief that an explicit module for long-term planning would be advantageous for learning hierarchical dependencies. However, these methods demand an excessive number of environment interactions or large model sizes, limiting their practicality. In this work, we demonstrate that proximal policy optimization (PPO), a simple yet versatile model-free algorithm, outperforms previous methods when optimized with recent implementation practices. Moreover, we find that the PPO agent can predict the next achievement to be unlocked to some extent, albeit with limited confidence. Based on this observation, we introduce a novel contrastive learning method, called achievement distillation, which strengthens the agent's ability to predict the next achievement. Our method exhibits a strong capacity for discovering hierarchical achievements and shows state-of-the-art performance on the challenging Crafter environment in a sample-efficient manner while utilizing fewer model parameters.

Regression on manifolds, and, more broadly, statistics on manifolds, has garnered significant importance in recent years due to the vast number of applications for this type of data. Circular data is a classic example, but so is data in the space of covariance matrices, data on the Grassmannian manifold obtained as a result of principal component analysis, among many others. In this work we investigate prediction sets for regression scenarios when the response variable, denoted by $Y$, resides in a manifold, and the covariable, denoted by X, lies in Euclidean space. This extends the concepts delineated in [Lei and Wasserman, 2014] to this novel context. Aligning with traditional principles in conformal inference, these prediction sets are distribution-free, indicating that no specific assumptions are imposed on the joint distribution of $(X, Y)$, and they maintain a non-parametric character. We prove the asymptotic almost sure convergence of the empirical version of these regions on the manifold to their population counterparts. The efficiency of this method is shown through a comprehensive simulation study and an analysis involving real-world data.

Offline policy learning is aimed at learning decision-making policies using existing datasets of trajectories without collecting additional data. The primary motivation for using reinforcement learning (RL) instead of supervised learning techniques such as behavior cloning is to find a policy that achieves a higher average return than the trajectories constituting the dataset. However, we empirically find that when a dataset is dominated by suboptimal trajectories, state-of-the-art offline RL algorithms do not substantially improve over the average return of trajectories in the dataset. We argue this is due to an assumption made by current offline RL algorithms of staying close to the trajectories in the dataset. If the dataset primarily consists of sub-optimal trajectories, this assumption forces the policy to mimic the suboptimal actions. We overcome this issue by proposing a sampling strategy that enables the policy to only be constrained to ``good data" rather than all actions in the dataset (i.e., uniform sampling). We present a realization of the sampling strategy and an algorithm that can be used as a plug-and-play module in standard offline RL algorithms. Our evaluation demonstrates significant performance gains in 72 imbalanced datasets, D4RL dataset, and across three different offline RL algorithms. Code is available at https://github.com/Improbable-AI/dw-offline-rl.

Goal-conditioned reinforcement learning (GCRL) refers to learning general-purpose skills that aim to reach diverse goals. In particular, offline GCRL only requires purely pre-collected datasets to perform training tasks without additional interactions with the environment. Although offline GCRL has become increasingly prevalent and many previous works have demonstrated its empirical success, the theoretical understanding of efficient offline GCRL algorithms is not well established, especially when the state space is huge and the offline dataset only covers the policy we aim to learn. In this paper, we provide a rigorous theoretical analysis of an existing empirically successful offline GCRL algorithm. We prove that under slight modification, this algorithm enjoys an $\widetilde{O}(\text{poly}(1/\epsilon))$ sample complexity (where $\epsilon$ is the desired suboptimality of the learned policy) with general function approximation thanks to the property of (semi-)strong convexity of the objective functions. We only require nearly minimal assumptions on the dataset (single-policy concentrability) and the function class (realizability). Moreover, this algorithm consists of two uninterleaved optimization steps, which we refer to as $V$-learning and policy learning, and is computationally stable since it does not involve minimax optimization. We also empirically validate our theory by showing that the modified algorithm outperforms the previous algorithm in various real-world environments. To the best of our knowledge, this is the first algorithm that is both provably efficient with general function approximation and single-policy concentrability, and empirically successful without requiring solving minimax optimization problems.  ( 3 min )

Poisoning of data sets is a potential security threat to large language models that can lead to backdoored models. A description of the internal mechanisms of backdoored language models and how they process trigger inputs, e.g., when switching to toxic language, has yet to be found. In this work, we study the internal representations of transformer-based backdoored language models and determine early-layer MLP modules as most important for the backdoor mechanism in combination with the initial embedding projection. We use this knowledge to remove, insert, and modify backdoor mechanisms with engineered replacements that reduce the MLP module outputs to essentials for the backdoor mechanism. To this end, we introduce PCP ablation, where we replace transformer modules with low-rank matrices based on the principal components of their activations. We demonstrate our results on backdoored toy, backdoored large, and non-backdoored open-source models. We show that we can improve the backdoor robustness of large language models by locally constraining individual modules during fine-tuning on potentially poisonous data sets. Trigger warning: Offensive language.

We propose a time series forecasting method named Quantum Gramian Angular Field (QGAF). This approach merges the advantages of quantum computing technology with deep learning, aiming to enhance the precision of time series classification and forecasting. We successfully transformed stock return time series data into two-dimensional images suitable for Convolutional Neural Network (CNN) training by designing specific quantum circuits. Distinct from the classical Gramian Angular Field (GAF) approach, QGAF's uniqueness lies in eliminating the need for data normalization and inverse cosine calculations, simplifying the transformation process from time series data to two-dimensional images. To validate the effectiveness of this method, we conducted experiments on datasets from three major stock markets: the China A-share market, the Hong Kong stock market, and the US stock market. Experimental results revealed that compared to the classical GAF method, the QGAF approach significantly improved time series prediction accuracy, reducing prediction errors by an average of 25% for Mean Absolute Error (MAE) and 48% for Mean Squared Error (MSE). This research confirms the potential and promising prospects of integrating quantum computing with deep learning techniques in financial time series forecasting.

Interpretable policy learning seeks to estimate intelligible decision policies from observed actions; however, existing models fall short by forcing a tradeoff between accuracy and interpretability. This tradeoff limits data-driven interpretations of human decision-making process. e.g. to audit medical decisions for biases and suboptimal practices, we require models of decision processes which provide concise descriptions of complex behaviors. Fundamentally, existing approaches are burdened by this tradeoff because they represent the underlying decision process as a universal policy, when in fact human decisions are dynamic and can change drastically with contextual information. Thus, we propose Contextualized Policy Recovery (CPR), which re-frames the problem of modeling complex decision processes as a multi-task learning problem in which complex decision policies are comprised of context-specific policies. CPR models each context-specific policy as a linear observation-to-action mapping, and generates new decision models $\textit{on-demand}$ as contexts are updated with new observations. CPR is compatible with fully offline and partially observable decision environments, and can be tailored to incorporate any recurrent black-box model or interpretable decision model. We assess CPR through studies on simulated and real data, achieving state-of-the-art performance on the canonical tasks of predicting antibiotic prescription in intensive care units ($+22\%$ AUROC vs. previous SOTA) and predicting MRI prescription for Alzheimer's patients ($+7.7\%$ AUROC vs. previous SOTA). With this improvement in predictive performance, CPR closes the accuracy gap between interpretable and black-box methods for policy learning, allowing high-resolution exploration and analysis of context-specific decision models.

Recent research on time-series self-supervised models shows great promise in learning semantic representations. However, it has been limited to small-scale datasets, e.g., thousands of temporal sequences. In this work, we make key technical contributions that are tailored to the numerical properties of time-series data and allow the model to scale to large datasets, e.g., millions of temporal sequences. We adopt the Transformer architecture by first partitioning the input into non-overlapping windows. Each window is then characterized by its normalized shape and two scalar values denoting the mean and standard deviation within each window. To embed scalar values that may possess arbitrary numerical scales to high-dimensional vectors, we propose a numerically multi-scaled embedding module enumerating all possible scales for the scalar values. The model undergoes pretraining using the proposed numerically multi-scaled embedding with a simple contrastive objective on a large-scale dataset containing over a million sequences. We study its transfer performance on a number of univariate and multivariate classification benchmarks. Our method exhibits remarkable improvement against previous representation learning approaches and establishes the new state of the art, even compared with domain-specific non-learning-based methods.

Multimodal sensors provide complementary information to develop accurate machine-learning methods for human activity recognition (HAR), but introduce significantly higher computational load, which reduces efficiency. This paper proposes an efficient multimodal neural architecture for HAR using an RGB camera and inertial measurement units (IMUs) called Multimodal Temporal Segment Attention Network (MMTSA). MMTSA first transforms IMU sensor data into a temporal and structure-preserving gray-scale image using the Gramian Angular Field (GAF), representing the inherent properties of human activities. MMTSA then applies a multimodal sparse sampling method to reduce data redundancy. Lastly, MMTSA adopts an inter-segment attention module for efficient multimodal fusion. Using three well-established public datasets, we evaluated MMTSA's effectiveness and efficiency in HAR. Results show that our method achieves superior performance improvements 11.13% of cross-subject F1-score on the MMAct dataset than the previous state-of-the-art (SOTA) methods. The ablation study and analysis suggest that MMTSA's effectiveness in fusing multimodal data for accurate HAR. The efficiency evaluation on an edge device showed that MMTSA achieved significantly better accuracy, lower computational load, and lower inference latency than SOTA methods.  ( 2 min )

Unmanned Aerial Vehicles (UAVs) are considered cutting-edge technology with highly cost-effective and flexible usage scenarios. Although many papers have reviewed the application of UAVs in agriculture, the review of the application for tree detection is still insufficient. This paper focuses on tree detection methods applied to UAV data collected by UAVs. There are two kinds of data, the point cloud and the images, which are acquired by the Light Detection and Ranging (LiDAR) sensor and camera, respectively. Among the detection methods using point-cloud data, this paper mainly classifies these methods according to LiDAR and Digital Aerial Photography (DAP). For the detection methods using images directly, this paper reviews these methods by whether or not to use the Deep Learning (DL) method. Our review concludes and analyses the comparison and combination between the application of LiDAR-based and DAP-based point cloud data. The performance, relative merits, and application fields of the methods are also introduced. Meanwhile, this review counts the number of tree detection studies using different methods in recent years. From our statics, the detection task using DL methods on the image has become a mainstream trend as the number of DL-based detection researches increases to 45% of the total number of tree detection studies up to 2022. As a result, this review could help and guide researchers who want to carry out tree detection on specific forests and for farmers to use UAVs in managing agriculture production.

We study core-set construction algorithms for the task of Diversity Maximization under fairness/partition constraint. Given a set of points $P$ in a metric space partitioned into $m$ groups, and given $k_1,\ldots,k_m$, the goal of this problem is to pick $k_i$ points from each group $i$ such that the overall diversity of the $k=\sum_i k_i$ picked points is maximized. We consider two natural diversity measures: sum-of-pairwise distances and sum-of-nearest-neighbor distances, and show improved core-set construction algorithms with respect to these measures. More precisely, we show the first constant factor core-set w.r.t. sum-of-pairwise distances whose size is independent of the size of the dataset and the aspect ratio. Second, we show the first core-set w.r.t. the sum-of-nearest-neighbor distances. Finally, we run several experiments showing the effectiveness of our core-set approach. In particular, we apply constrained diversity maximization to summarize a set of timed messages that takes into account the messages' recency. Specifically, the summary should include more recent messages compared to older ones. This is a real task in one of the largest communication platforms, affecting the experience of hundreds of millions daily active users. By utilizing our core-set method for this task, we achieve a 100x speed-up while losing the diversity by only a few percent. Moreover, our approach allows us to improve the space usage of the algorithm in the streaming setting.

Recently, Meta-Black-Box Optimization with Reinforcement Learning (MetaBBO-RL) has showcased the power of leveraging RL at the meta-level to mitigate manual fine-tuning of low-level black-box optimizers. However, this field is hindered by the lack of a unified benchmark. To fill this gap, we introduce MetaBox, the first benchmark platform expressly tailored for developing and evaluating MetaBBO-RL methods. MetaBox offers a flexible algorithmic template that allows users to effortlessly implement their unique designs within the platform. Moreover, it provides a broad spectrum of over 300 problem instances, collected from synthetic to realistic scenarios, and an extensive library of 19 baseline methods, including both traditional black-box optimizers and recent MetaBBO-RL methods. Besides, MetaBox introduces three standardized performance metrics, enabling a more thorough assessment of the methods. In a bid to illustrate the utility of MetaBox for facilitating rigorous evaluation and in-depth analysis, we carry out a wide-ranging benchmarking study on existing MetaBBO-RL methods. Our MetaBox is open-source and accessible at: https://github.com/GMC-DRL/MetaBox.

Motivated by the computational difficulties incurred by popular deep learning algorithms for the generative modeling of temporal densities, we propose a cheap alternative which requires minimal hyperparameter tuning and scales favorably to high dimensional problems. In particular, we use a projection-based optimal transport solver [Meng et al., 2019] to join successive samples and subsequently use transport splines [Chewi et al., 2020] to interpolate the evolving density. When the sampling frequency is sufficiently high, the optimal maps are close to the identity and are thus computationally efficient to compute. Moreover, the training process is highly parallelizable as all optimal maps are independent and can thus be learned simultaneously. Finally, the approach is based solely on numerical linear algebra rather than minimizing a nonconvex objective function, allowing us to easily analyze and control the algorithm. We present several numerical experiments on both synthetic and real-world datasets to demonstrate the efficiency of our method. In particular, these experiments show that the proposed approach is highly competitive compared with state-of-the-art normalizing flows conditioned on time across a wide range of dimensionalities.

This study proposes an interpretable neural network-based non-proportional odds model (N$^3$POM) for ordinal regression. N$^3$POM is different from conventional approaches to ordinal regression with non-proportional models in several ways: (1) N$^3$POM is designed to directly handle continuous responses, whereas standard methods typically treat de facto ordered continuous variables as discrete, (2) instead of estimating response-dependent finite coefficients of linear models from discrete responses as is done in conventional approaches, we train a non-linear neural network to serve as a coefficient function. Thanks to the neural network, N$^3$POM offers flexibility while preserving the interpretability of conventional ordinal regression. We establish a sufficient condition under which the predicted conditional cumulative probability locally satisfies the monotonicity constraint over a user-specified region in the covariate space. Additionally, we provide a monotonicity-preserving stochastic (MPS) algorithm for effectively training the neural network. We apply N$^3$POM to several real-world datasets.

Training machine learning models robust to distribution shifts is critical for real-world applications. Some robust training algorithms (e.g., Group DRO) specialize to group shifts and require group information on all training points. Other methods (e.g., CVaR DRO) that do not need group annotations can be overly conservative, since they naively upweight high loss points which may form a contrived set that does not correspond to any meaningful group in the real world (e.g., when the high loss points are randomly mislabeled training points). In this work, we address limitations in prior approaches by assuming a more nuanced form of group shift: conditioned on the label, we assume that the true group function (indicator over group) is simple. For example, we may expect that group shifts occur along low bitrate features (e.g., image background, lighting). Thus, we aim to learn a model that maintains high accuracy on simple group functions realized by these low bitrate features, that need not spend valuable model capacity achieving high accuracy on contrived groups of examples. Based on this, we consider the two-player game formulation of DRO where the adversary's capacity is bitrate-constrained. Our resulting practical algorithm, Bitrate-Constrained DRO (BR-DRO), does not require group information on training samples yet matches the performance of Group DRO on datasets that have training group annotations and that of CVaR DRO on long-tailed distributions. Our theoretical analysis reveals that in some settings BR-DRO objective can provably yield statistically efficient and less conservative solutions than unconstrained CVaR DRO.

Attention mechanisms have exhibited promising potential in enhancing learning models by identifying salient portions of input data. This is particularly valuable in scenarios where limited training samples are accessible due to challenges in data collection and labeling. Drawing inspiration from human recognition processes, we posit that an AI baseline's performance could be more accurate and dependable if it is exposed to essential segments of raw data rather than the entire input dataset, akin to human perception. However, the task of selecting these informative data segments, referred to as hard attention finding, presents a formidable challenge. In situations with few training samples, existing studies struggle to locate such informative regions due to the large number of training parameters that cannot be effectively learned from the available limited samples. In this study, we introduce a novel and practical framework for achieving explainable hard attention finding, specifically tailored for few-shot learning scenarios, called FewXAT. Our approach employs deep reinforcement learning to implement the concept of hard attention, directly impacting raw input data and thus rendering the process interpretable for human understanding. Through extensive experimentation across various benchmark datasets, we demonstrate the efficacy of our proposed method.

A flow control system is a critical concept for increasing the production capacity of manufacturing systems. To solve the scheduling optimization problem related to the flow control with the aim of improving productivity, existing methods depend on a heuristic design by domain human experts. Therefore, the methods require correction, monitoring, and verification by using real equipment. As system designs increase in complexity, the monitoring time increases, which decreases the probability of arriving at the optimal design. As an alternative approach to the heuristic design of flow control systems, the use of deep reinforcement learning to solve the scheduling optimization problem has been considered. Although the existing research on reinforcement learning has yielded excellent performance in some areas, the applicability of the results to actual FAB such as display and semiconductor manufacturing processes is not evident so far. To this end, we propose a method to implement a physical simulation environment and devise a feasible flow control system design using a transfer robot in display manufacturing through reinforcement learning. We present a model and parameter setting to build a virtual environment for different display transfer robots, and training methods of reinforcement learning on the environment to obtain an optimal scheduling of glass flow control systems. Its feasibility was verified by using different types of robots used in the actual process.

Riemannian geometry provides us with powerful tools to explore the latent space of generative models while preserving the underlying structure of the data. The latent space can be equipped it with a Riemannian metric, pulled back from the data manifold. With this metric, we can systematically navigate the space relying on geodesics defined as the shortest curves between two points. Generative models are often stochastic, causing the data space, the Riemannian metric, and the geodesics, to be stochastic as well. Stochastic objects are at best impractical, and at worst impossible, to manipulate. A common solution is to approximate the stochastic pullback metric by its expectation. But the geodesics derived from this expected Riemannian metric do not correspond to the expected length-minimising curves. In this work, we propose another metric whose geodesics explicitly minimise the expected length of the pullback metric. We show this metric defines a Finsler metric, and we compare it with the expected Riemannian metric. In high dimensions, we prove that both metrics converge to each other at a rate of $O\left(\frac{1}{D}\right)$. This convergence implies that the established expected Riemannian metric is an accurate approximation of the theoretically more grounded Finsler metric. This provides justification for using the expected Riemannian metric for practical implementations.

Partially observable Markov decision processes (POMDPs) have been widely applied to capture many real-world applications. However, existing theoretical results have shown that learning in general POMDPs could be intractable, where the main challenge lies in the lack of latent state information. A key fundamental question here is how much online state information (OSI) is sufficient to achieve tractability. In this paper, we establish a lower bound that reveals a surprising hardness result: unless we have full OSI, we need an exponentially scaling sample complexity to obtain an $\epsilon$-optimal policy solution for POMDPs. Nonetheless, inspired by the key insights in our lower bound design, we find that there exist important tractable classes of POMDPs even with only partial OSI. In particular, for two novel classes of POMDPs with partial OSI, we provide new algorithms that are proved to be near-optimal by establishing new regret upper and lower bounds.

This work analyzes Graph Neural Networks, a generalization of Fully-Connected Deep Neural Nets on Graph structured data, when their width, that is the number of nodes in each fullyconnected layer is increasing to infinity. Infinite Width Neural Networks are connecting Deep Learning to Gaussian Processes and Kernels, both Machine Learning Frameworks with long traditions and extensive theoretical foundations. Gaussian Processes and Kernels have much less hyperparameters then Neural Networks and can be used for uncertainty estimation, making them more user friendly for applications. This works extends the increasing amount of research connecting Gaussian Processes and Kernels to Neural Networks. The Kernel and Gaussian Process closed forms are derived for a variety of architectures, namely the standard Graph Neural Network, the Graph Neural Network with Skip-Concatenate Connections and the Graph Attention Neural Network. All architectures are evaluated on a variety of datasets on the task of transductive Node Regression and Classification. Additionally, a Spectral Sparsification method known as Effective Resistance is used to improve runtime and memory requirements. Extending the setting to inductive graph learning tasks (Graph Regression/ Classification) is straightforward and is briefly discussed in 3.5.

In his breakthrough paper, Raz showed that any parity learning algorithm requires either quadratic memory or an exponential number of samples [FOCS'16, JACM'19]. A line of work that followed extended this result to a large class of learning problems. Until recently, all these results considered learning in the streaming model, where each sample is drawn independently, and the learner is allowed a single pass over the stream of samples. Garg, Raz, and Tal [CCC'19] considered a stronger model, allowing multiple passes over the stream. In the $2$-pass model, they showed that learning parities of size $n$ requires either a memory of size $n^{1.5}$ or at least $2^{\sqrt{n}}$ samples. (Their result also generalizes to other learning problems.) In this work, for any constant $q$, we prove tight memory-sample lower bounds for any parity learning algorithm that makes $q$ passes over the stream of samples. We show that such a learner requires either $\Omega(n^{2})$ memory size or at least $2^{\Omega(n)}$ samples. Beyond establishing a tight lower bound, this is the first non-trivial lower bound for $q$-pass learning for any $q\ge 3$. Similar to prior work, our results extend to any learning problem with many nearly-orthogonal concepts. We complement the lower bound with an upper bound, showing that parity learning with $q$ passes can be done efficiently with $O(n^2/\log q)$ memory.

Secure inference of deep convolutional neural networks (CNNs) under RNS-CKKS involves polynomial approximation of unsupported non-linear activation functions. However, existing approaches have three main limitations: 1) Inflexibility: The polynomial approximation and associated homomorphic evaluation architecture are customized manually for each CNN architecture and do not generalize to other networks. 2) Suboptimal Approximation: Each activation function is approximated instead of the function represented by the CNN. 3) Restricted Design: Either high-degree or low-degree polynomial approximations are used. The former retains high accuracy but slows down inference due to bootstrapping operations, while the latter accelerates ciphertext inference but compromises accuracy. To address these limitations, we present AutoFHE, which automatically adapts standard CNNs for secure inference under RNS-CKKS. The key idea is to adopt layerwise mixed-degree polynomial activation functions, which are optimized jointly with the homomorphic evaluation architecture in terms of the placement of bootstrapping operations. The problem is modeled within a multi-objective optimization framework to maximize accuracy and minimize the number of bootstrapping operations. AutoFHE can be applied flexibly on any CNN architecture, and it provides diverse solutions that span the trade-off between accuracy and latency. Experimental evaluation over RNS-CKKS encrypted CIFAR datasets shows that AutoFHE accelerates secure inference by $1.32\times$ to $1.8\times$ compared to methods employing high-degree polynomials. It also improves accuracy by up to 2.56% compared to methods using low-degree polynomials. Lastly, AutoFHE accelerates inference and improves accuracy by $103\times$ and 3.46%, respectively, compared to CNNs under TFHE.

Incomplete knowledge of the environment leads an agent to make decisions under uncertainty. One of the major dilemmas in Reinforcement Learning (RL) where an autonomous agent has to balance two contrasting needs in making its decisions is: exploiting the current knowledge of the environment to maximize the cumulative reward as well as exploring actions that allow improving the knowledge of the environment, hopefully leading to higher reward values (exploration-exploitation trade-off). Concurrently, another relevant issue regards the full observability of the states, which may not be assumed in all applications. Such as when only 2D images are considered as input in a RL approach used for finding the optimal action within a 3D simulation environment. In this work, we address these issues by deploying and testing several techniques to balance exploration and exploitation trade-off on partially observable systems for predicting steering wheels in autonomous driving scenario. More precisely, the final aim is to investigate the effects of using both stochastic and deterministic multi-armed bandit strategies coupled with a Deep Recurrent Q-Network. Additionally, we adapted and evaluated the impact of an innovative method to improve the learning phase of the underlying Convolutional Recurrent Neural Network. We aim to show that adaptive stochastic methods for exploration better approximate the trade-off between exploration and exploitation as, in general, Softmax and Max-Boltzmann strategies are able to outperform epsilon-greedy techniques.

Future sequence represents the outcome after executing the action into the environment. When driven by the information-theoretic concept of mutual information, it seeks maximally informative consequences. Explicit outcomes may vary across state, return, or trajectory serving different purposes such as credit assignment or imitation learning. However, the inherent nature of incorporating intrinsic motivation with reward maximization is often neglected. In this work, we propose a variational approach to jointly learn the necessary quantity for estimating the mutual information and the dynamics model, providing a general framework for incorporating different forms of outcomes of interest. Integrated into a policy iteration scheme, our approach guarantees convergence to the optimal policy. While we mainly focus on theoretical analysis, our approach opens the possibilities of leveraging intrinsic control with model learning to enhance sample efficiency and incorporate uncertainty of the environment into decision-making.

Diffusion models suffer from slow sample generation at inference time. Therefore, developing a principled framework for fast deterministic/stochastic sampling for a broader class of diffusion models is a promising direction. We propose two complementary frameworks for accelerating sample generation in pre-trained models: Conjugate Integrators and Splitting Integrators. Conjugate integrators generalize DDIM, mapping the reverse diffusion dynamics to a more amenable space for sampling. In contrast, splitting-based integrators, commonly used in molecular dynamics, reduce the numerical simulation error by cleverly alternating between numerical updates involving the data and auxiliary variables. After extensively studying these methods empirically and theoretically, we present a hybrid method that leads to the best-reported performance for diffusion models in augmented spaces. Applied to Phase Space Langevin Diffusion [Pandey & Mandt, 2023] on CIFAR-10, our deterministic and stochastic samplers achieve FID scores of 2.11 and 2.36 in only 100 network function evaluations (NFE) as compared to 2.57 and 2.63 for the best-performing baselines, respectively. Our code and model checkpoints will be made publicly available at \url{https://github.com/mandt-lab/PSLD}.

In this paper, we investigate the practical relevance of explainable artificial intelligence (XAI) with a special focus on the producing industries and relate them to the current state of academic XAI research. Our findings are based on an extensive series of interviews regarding the role and applicability of XAI along the Machine Learning (ML) lifecycle in current industrial practice and its expected relevance in the future. The interviews were conducted among a great variety of roles and key stakeholders from different industry sectors. On top of that, we outline the state of XAI research by providing a concise review of the relevant literature. This enables us to provide an encompassing overview covering the opinions of the surveyed persons as well as the current state of academic research. By comparing our interview results with the current research approaches we reveal several discrepancies. While a multitude of different XAI approaches exists, most of them are centered around the model evaluation phase and data scientists. Their versatile capabilities for other stages are currently either not sufficiently explored or not popular among practitioners. In line with existing work, our findings also confirm that more efforts are needed to enable also non-expert users' interpretation and understanding of opaque AI models with existing methods and frameworks.

Seeing-eye robots are very useful tools for guiding visually impaired people, potentially producing a huge societal impact given the low availability and high cost of real guide dogs. Although a few seeing-eye robot systems have already been demonstrated, none considered external tugs from humans, which frequently occur in a real guide dog setting. In this paper, we simultaneously train a locomotion controller that is robust to external tugging forces via Reinforcement Learning (RL), and an external force estimator via supervised learning. The controller ensures stable walking, and the force estimator enables the robot to respond to the external forces from the human. These forces are used to guide the robot to the global goal, which is unknown to the robot, while the robot guides the human around nearby obstacles via a local planner. Experimental results in simulation and on hardware show that our controller is robust to external forces, and our seeing-eye system can accurately detect force direction. We demonstrate our full seeing-eye robot system on a real quadruped robot with a blindfolded human. The video can be seen at our project page: https://bu-air-lab.github.io/guide_dog/

In this work, we leverage pre-trained Large Language Models (LLMs) to enhance time-series forecasting. Mirroring the growing interest in unifying models for Natural Language Processing and Computer Vision, we envision creating an analogous model for long-term time-series forecasting. Due to limited large-scale time-series data for building robust foundation models, our approach LLM4TS focuses on leveraging the strengths of pre-trained LLMs. By combining time-series patching with temporal encoding, we have enhanced the capability of LLMs to handle time-series data effectively. Inspired by the supervised fine-tuning in chatbot domains, we prioritize a two-stage fine-tuning process: first conducting supervised fine-tuning to orient the LLM towards time-series data, followed by task-specific downstream fine-tuning. Furthermore, to unlock the flexibility of pre-trained LLMs without extensive parameter adjustments, we adopt several Parameter-Efficient Fine-Tuning (PEFT) techniques. Drawing on these innovations, LLM4TS has yielded state-of-the-art results in long-term forecasting. Our model has also shown exceptional capabilities as both a robust representation learner and an effective few-shot learner, thanks to the knowledge transferred from the pre-trained LLM.

Different conflicting optimization criteria arise naturally in various Deep Learning scenarios. These can address different main tasks (i.e., in the setting of Multi-Task Learning), but also main and secondary tasks such as loss minimization versus sparsity. The usual approach is a simple weighting of the criteria, which formally only works in the convex setting. In this paper, we present a Multi-Objective Optimization algorithm using a modified Weighted Chebyshev scalarization for training Deep Neural Networks (DNNs) with respect to several tasks. By employing this scalarization technique, the algorithm can identify all optimal solutions of the original problem while reducing its complexity to a sequence of single-objective problems. The simplified problems are then solved using an Augmented Lagrangian method, enabling the use of popular optimization techniques such as Adam and Stochastic Gradient Descent, while efficaciously handling constraints. Our work aims to address the (economical and also ecological) sustainability issue of DNN models, with a particular focus on Deep Multi-Task models, which are typically designed with a very large number of weights to perform equally well on multiple tasks. Through experiments conducted on two Machine Learning datasets, we demonstrate the possibility of adaptively sparsifying the model during training without significantly impacting its performance, if we are willing to apply task-specific adaptations to the network weights. Code is available at https://github.com/salomonhotegni/MDMTN.

In continual learning, the learner learns multiple tasks in sequence, with data being acquired only once for each task. Catastrophic forgetting is a major challenge to continual learning. To reduce forgetting, some existing rehearsal-based methods use episodic memory to replay samples of previous tasks. However, in the process of knowledge integration when learning a new task, this strategy also suffers from catastrophic forgetting due to an imbalance between old and new knowledge. To address this problem, we propose a novel replay strategy called Manifold Expansion Replay (MaER). We argue that expanding the implicit manifold of the knowledge representation in the episodic memory helps to improve the robustness and expressiveness of the model. To this end, we propose a greedy strategy to keep increasing the diameter of the implicit manifold represented by the knowledge in the buffer during memory management. In addition, we introduce Wasserstein distance instead of cross entropy as distillation loss to preserve previous knowledge. With extensive experimental validation on MNIST, CIFAR10, CIFAR100, and TinyImageNet, we show that the proposed method significantly improves the accuracy in continual learning setup, outperforming the state of the arts.

To solve complex tasks, large language models (LLMs) often require multiple rounds of interactions with the user, sometimes assisted by external tools. However, current evaluation protocols often emphasize benchmark performance with single-turn exchanges, neglecting the nuanced interactions among the user, LLMs, and external tools, while also underestimating the importance of natural language feedback from users. These oversights contribute to discrepancies between research benchmark evaluations and real-world use cases. We introduce MINT, a benchmark that evaluates LLMs' ability to solve tasks with multi-turn interactions by (1) using tools and (2) leveraging natural language feedback. To ensure reproducibility, we provide an evaluation framework where LLMs can access tools by executing Python code and receive users' natural language feedback simulated by GPT-4. We repurpose a diverse set of established evaluation datasets focusing on reasoning, coding, and decision-making and carefully curate them into a compact subset for efficient evaluation. Our analysis of 20 open- and closed-source LLMs offers intriguing findings. (a) LLMs generally benefit from tools and language feedback, with performance gains (absolute, same below) of 1-8% for each turn of tool use and 2-17% with natural language feedback. (b) Better single-turn performance does not guarantee better multi-turn performance. (c) Surprisingly, on the LLMs evaluated, supervised instruction-finetuning (SIFT) and reinforcement learning from human feedback (RLHF) generally hurt multi-turn capabilities. We expect MINT can help measure progress and incentivize research in improving LLMs' capabilities in multi-turn interactions, especially for open-source communities where multi-turn human evaluation can be less accessible compared to commercial LLMs with a larger user base.

Exploring variations of 3D shapes is a time-consuming process in traditional 3D modeling tools. Deep generative models of 3D shapes often feature continuous latent spaces that can, in principle, be used to explore potential variations starting from a set of input shapes. In practice, doing so can be problematic: latent spaces are high dimensional and hard to visualize, contain shapes that are not relevant to the input shapes, and linear paths through them often lead to sub-optimal shape transitions. Furthermore, one would ideally be able to explore variations in the original high-quality meshes used to train the generative model, not its lower-quality output geometry. In this paper, we present a method to explore variations among a given set of landmark shapes by constructing a mapping from an easily-navigable 2D exploration space to a subspace of a pre-trained generative model. We first describe how to find a mapping that spans the set of input landmark shapes and exhibits smooth variations between them. We then show how to turn the variations in this subspace into deformation fields, to transfer those variations to high-quality meshes for the landmark shapes. Our results show that our method can produce visually-pleasing and easily-navigable 2D exploration spaces for several different shape categories, especially as compared to prior work on learning deformation spaces for 3D shapes.

Learning transparent, interpretable controllers with offline data in decision-making systems is an essential area of research due to its potential to reduce the risk of applications in real-world systems. However, in responsibility-sensitive settings such as healthcare, decision accountability is of paramount importance, yet has not been adequately addressed by the literature. This paper introduces the Accountable Offline Controller (AOC) that employs the offline dataset as the Decision Corpus and performs accountable control based on a tailored selection of examples, referred to as the Corpus Subset. ABC operates effectively in low-data scenarios, can be extended to the strictly offline imitation setting, and displays qualities of both conservation and adaptability. We assess ABC's performance in both simulated and real-world healthcare scenarios, emphasizing its capability to manage offline control tasks with high levels of performance while maintaining accountability. Keywords: Interpretable Reinforcement Learning, Explainable Reinforcement Learning, Reinforcement Learning Transparency, Offline Reinforcement Learning, Batched Control.

Score-based Generative Models (SGMs) have demonstrated exceptional synthesis outcomes across various tasks. However, the current design landscape of the forward diffusion process remains largely untapped and often relies on physical heuristics or simplifying assumptions. Utilizing insights from the development of scalable Bayesian posterior samplers, we present a complete recipe for formulating forward processes in SGMs, ensuring convergence to the desired target distribution. Our approach reveals that several existing SGMs can be seen as specific manifestations of our framework. Building upon this method, we introduce Phase Space Langevin Diffusion (PSLD), which relies on score-based modeling within an augmented space enriched by auxiliary variables akin to physical phase space. Empirical results exhibit the superior sample quality and improved speed-quality trade-off of PSLD compared to various competing approaches on established image synthesis benchmarks. Remarkably, PSLD achieves sample quality akin to state-of-the-art SGMs (FID: 2.10 for unconditional CIFAR-10 generation). Lastly, we demonstrate the applicability of PSLD in conditional synthesis using pre-trained score networks, offering an appealing alternative as an SGM backbone for future advancements. Code and model checkpoints can be accessed at \url{https://github.com/mandt-lab/PSLD}.

Large language models (LLMs) aligned to human preferences via reinforcement learning from human feedback (RLHF) underpin many commercial applications. However, how RLHF impacts LLM internals remains opaque. We propose a novel method to interpret learned reward functions in RLHF-tuned LLMs using sparse autoencoders. Our approach trains autoencoder sets on activations from a base LLM and its RLHF-tuned version. By comparing autoencoder hidden spaces, we identify unique features that reflect the accuracy of the learned reward model. To quantify this, we construct a scenario where the tuned LLM learns token-reward mappings to maximize reward. This is the first application of sparse autoencoders for interpreting learned rewards and broadly inspecting reward learning in LLMs. Our method provides an abstract approximation of reward integrity. This presents a promising technique for ensuring alignment between specified objectives and model behaviors.

Recent developments in offline reinforcement learning have uncovered the immense potential of diffusion modeling, which excels at representing heterogeneous behavior policies. However, sampling from diffusion policies is considerably slow because it necessitates tens to hundreds of iterative inference steps for one action. To address this issue, we propose to extract an efficient deterministic inference policy from critic models and pretrained diffusion behavior models, leveraging the latter to directly regularize the policy gradient with the behavior distribution's score function during optimization. Our method enjoys powerful generative capabilities of diffusion modeling while completely circumventing the computationally intensive and time-consuming diffusion sampling scheme, both during training and evaluation. Extensive results on D4RL tasks show that our method boosts action sampling speed by more than 25 times compared with various leading diffusion-based methods in locomotion tasks, while still maintaining state-of-the-art performance.

Most bandit algorithms assume that the reward variances or their upper bounds are known, and that they are the same for all arms. This naturally leads to suboptimal performance and higher regret due to variance overestimation. On the other hand, underestimated reward variances may lead to linear regret due to committing early to a suboptimal arm. This motivated prior works on variance-adaptive frequentist algorithms, which have strong instance-dependent regret bounds but cannot incorporate prior knowledge on reward variances. We lay foundations for the Bayesian setting, which incorporates prior knowledge. This results in lower regret in practice, due to using the prior in the algorithm design, and also improved regret guarantees. Specifically, we study Gaussian bandits with {unknown heterogeneous reward variances}, and develop a Thompson sampling algorithm with prior-dependent Bayes regret bounds. We achieve lower regret with lower reward variances and more informative priors on them, which is precisely why we pay only for what is uncertain. This is the first result of its kind. Finally, we corroborate our theory with extensive experiments, which show the superiority of our variance-adaptive Bayesian algorithm over prior frequentist approaches. We also show that our approach is robust to model misspecification and can be applied with estimated priors.

Adversarial attacks can affect the object recognition capabilities of machines in wild. These can often result from spurious correlations between input and class labels, and are prone to memorization in large networks. While networks are expected to do automated feature selection, it is not effective at the scale of the object. Humans, however, are able to select the minimum set of features required to form a robust representation of an object. In this work, we show that finetuning any pretrained off-the-shelf network with Extreme Image Transformations (EIT) not only helps in learning a robust latent representation, it also improves the performance of these networks against common adversarial attacks of various intensities. Our EIT trained networks show strong activations in the object regions even when tested with more intense noise, showing promising generalizations across different kinds of adversarial attacks.

By circumventing the resolution limitations of optics, coherent diffractive imaging (CDI) and ptychography are making their way into scientific fields ranging from X-ray imaging to astronomy. Yet, the need for time consuming iterative phase recovery hampers real-time imaging. While supervised deep learning strategies have increased reconstruction speed, they sacrifice image quality. Furthermore, these methods' demand for extensive labeled training data is experimentally burdensome. Here, we propose an unsupervised physics-informed neural network reconstruction method, PtychoPINN, that retains the factor of 100-to-1000 speedup of deep learning-based reconstruction while improving reconstruction quality by combining the diffraction forward map with real-space constraints from overlapping measurements. In particular, PtychoPINN significantly advances generalizability, accuracy (with a typical 10 dB PSNR increase), and linear resolution (2- to 6-fold gain). This blend of performance and speed offers exciting prospects for high-resolution real-time imaging in high-throughput environments such as X-ray free electron lasers (XFELs) and diffraction-limited light sources.

Machine unlearning, the study of efficiently removing the impact of specific training points on the trained model, has garnered increased attention of late, driven by the need to comply with privacy regulations like the Right to be Forgotten. Although unlearning is particularly relevant for LLMs in light of the copyright issues they raise, achieving precise unlearning is computationally infeasible for very large models. To this end, recent work has proposed several algorithms which approximate the removal of training data without retraining the model. These algorithms crucially rely on access to the model parameters in order to update them, an assumption that may not hold in practice due to computational constraints or when the LLM is accessed via API. In this work, we propose a new class of unlearning methods for LLMs we call ''In-Context Unlearning'', providing inputs in context and without having to update model parameters. To unlearn a particular training instance, we provide the instance alongside a flipped label and additional correctly labelled instances which are prepended as inputs to the LLM at inference time. Our experimental results demonstrate that these contexts effectively remove specific information from the training set while maintaining performance levels that are competitive with (or in some cases exceed) state-of-the-art unlearning methods that require access to the LLM parameters.

We present a lifelong audio-video masked autoencoder that continually learns the multimodal representations from a video stream containing audio-video pairs, while its distribution continually shifts over time. Specifically, we propose two novel ideas to tackle the problem: (1) Localized Alignment: We introduce a small trainable multimodal encoder that predicts the audio and video tokens that are well-aligned with each other. This allows the model to learn only the highly correlated audiovisual patches with accurate multimodal relationships. (2) Forget-robust multimodal patch selection: We compare the relative importance of each audio-video patch between the current and past data pair to mitigate unintended drift of the previously learned audio-video representations. Our proposed method, FLAVA (Forget-robust Localized Audio-Video Alignment), therefore, captures the complex relationships between the audio and video modalities during training on a sequence of pre-training tasks while alleviating the forgetting of learned audiovisual correlations. Our experiments validate that FLAVA outperforms the state-of-the-art continual learning methods on several benchmark datasets under continual audio-video representation learning scenarios.

Large language models (LLMs) often make factually incorrect responses despite their success in various applications. In this paper, we hypothesize that relying heavily on simple co-occurrence statistics of the pre-training corpora is one of the main factors that cause factual errors. Our results reveal that LLMs are vulnerable to the co-occurrence bias, defined as preferring frequently co-occurred words over the correct answer. Consequently, LLMs struggle to recall facts whose subject and object rarely co-occur in the pre-training dataset although they are seen during finetuning. We show that co-occurrence bias remains despite scaling up model sizes or finetuning. Therefore, we suggest finetuning on a debiased dataset to mitigate the bias by filtering out biased samples whose subject-object co-occurrence count is high. Although debiased finetuning allows LLMs to memorize rare facts in the training set, it is not effective in recalling rare facts unseen during finetuning. Further research in mitigation will help build reliable language models by preventing potential errors. The code is available at \url{https://github.com/CheongWoong/impact_of_cooccurrence}.

Language models and specialized table embedding models have recently demonstrated strong performance on many tasks over tabular data. Researchers and practitioners are keen to leverage these models in many new application contexts; but limited understanding of the strengths and weaknesses of these models, and the table representations they generate, makes the process of finding a suitable model for a given task reliant on trial and error. There is an urgent need to gain a comprehensive understanding of these models to minimize inefficiency and failures in downstream usage. To address this need, we propose Observatory, a formal framework to systematically analyze embedding representations of relational tables. Motivated both by invariants of the relational data model and by statistical considerations regarding data distributions, we define eight primitive properties, and corresponding measures to quantitatively characterize table embeddings for these properties. Based on these properties, we define an extensible framework to evaluate language and table embedding models. We collect and synthesize a suite of datasets and use Observatory to analyze seven such models. Our analysis provides insights into the strengths and weaknesses of learned representations over tables. We find, for example, that some models are sensitive to table structure such as column order, that functional dependencies are rarely reflected in embeddings, and that specialized table embedding models have relatively lower sample fidelity. Such insights help researchers and practitioners better anticipate model behaviors and select appropriate models for their downstream tasks, while guiding researchers in the development of new models.

Larger and deeper networks generalise well despite their increased capacity to overfit. Understanding why this happens is theoretically and practically important. One recent approach looks at the infinitely wide limits of such networks and their corresponding kernels. However, these theoretical tools cannot fully explain finite networks as the empirical kernel changes significantly during gradient-descent-based training in contrast to infinite networks. In this work, we derive an iterative linearised training method as a novel empirical tool to further investigate this distinction, allowing us to control for sparse (i.e. infrequent) feature updates and quantify the frequency of feature learning needed to achieve comparable performance. We justify iterative linearisation as an interpolation between a finite analog of the infinite width regime, which does not learn features, and standard gradient descent training, which does. Informally, we also show that it is analogous to a damped version of the Gauss-Newton algorithm -- a second-order method. We show that in a variety of cases, iterative linearised training surprisingly performs on par with standard training, noting in particular how much less frequent feature learning is required to achieve comparable performance. We also show that feature learning is essential for good performance. Since such feature learning inevitably causes changes in the NTK kernel, we provide direct negative evidence for the NTK theory, which states the NTK kernel remains constant during training.

In many cases, a machine learning model must learn to correctly predict a few data points with particular values of interest in a broader range of data where many target values are zero. Zero-inflated data can be found in diverse scenarios, such as lumpy and intermittent demands, power consumption for home appliances being turned on and off, impurities measurement in distillation processes, and even airport shuttle demand prediction. The presence of zeroes affects the models' learning and may result in poor performance. Furthermore, zeroes also distort the metrics used to compute the model's prediction quality. This paper showcases two real-world use cases (home appliances classification and airport shuttle demand prediction) where a hierarchical model applied in the context of zero-inflated data leads to excellent results. In particular, for home appliances classification, the weighted average of Precision, Recall, F1, and AUC ROC was increased by 27%, 34%, 49%, and 27%, respectively. Furthermore, it is estimated that the proposed approach is also four times more energy efficient than the SOTA approach against which it was compared to. Two-fold models performed best in all cases when predicting airport shuttle demand, and the difference against other models has been proven to be statistically significant.  ( 2 min )

Optical Character Recognition (OCR) is a widely used tool to extract text from scanned documents. Today, the state-of-the-art is achieved by exploiting deep neural networks. However, the cost of this performance is paid at the price of system vulnerability. For instance, in backdoor attacks, attackers compromise the training phase by inserting a backdoor in the victim's model that will be activated at testing time by specific patterns while leaving the overall model performance intact. This work proposes a backdoor attack for OCR resulting in the injection of non-readable characters from malicious input images. This simple but effective attack exposes the state-of-the-art OCR weakness, making the extracted text correct to human eyes but simultaneously unusable for the NLP application that uses OCR as a preprocessing step. Experimental results show that the attacked models successfully output non-readable characters for around 90% of the poisoned instances without harming their performance for the remaining instances.

We develop a novel framework that adds the regularizers of the sparse group lasso to a family of adaptive optimizers in deep learning, such as Momentum, Adagrad, Adam, AMSGrad, AdaHessian, and create a new class of optimizers, which are named Group Momentum, Group Adagrad, Group Adam, Group AMSGrad and Group AdaHessian, etc., accordingly. We establish theoretically proven convergence guarantees in the stochastic convex settings, based on primal-dual methods. We evaluate the regularized effect of our new optimizers on three large-scale real-world ad click datasets with state-of-the-art deep learning models. The experimental results reveal that compared with the original optimizers with the post-processing procedure which uses the magnitude pruning method, the performance of the models can be significantly improved on the same sparsity level. Furthermore, in comparison to the cases without magnitude pruning, our methods can achieve extremely high sparsity with significantly better or highly competitive performance. The code is available at https://github.com/intelligent-machine-learning/dlrover/blob/master/tfplus.  ( 3 min )

It is often advantageous to train models on a subset of the available train examples, because the examples are of variable quality or because one would like to train with fewer examples, without sacrificing performance. We present Gradient Information Optimization (GIO), a scalable, task-agnostic approach to this data selection problem that requires only a small set of (unlabeled) examples representing a target distribution. GIO begins from a natural, information-theoretic objective that is intractable in practice. Our contribution is in showing that it can be made highly scalable through a simple relaxation of the objective and a highly efficient implementation. In experiments with machine translation, spelling correction, and image recognition, we show that GIO delivers outstanding results with very small train sets. These findings are robust to different representation models and hyperparameters for GIO itself. GIO is task- and domain-agnostic and can be applied out-of-the-box to new datasets and domains.

Feature learning is thought to be one of the fundamental reasons for the success of deep neural networks. It is rigorously known that in two-layer fully-connected neural networks under certain conditions, one step of gradient descent on the first layer followed by ridge regression on the second layer can lead to feature learning; characterized by the appearance of a separated rank-one component -- spike -- in the spectrum of the feature matrix. However, with a constant gradient descent step size, this spike only carries information from the linear component of the target function and therefore learning non-linear components is impossible. We show that with a learning rate that grows with the sample size, such training in fact introduces multiple rank-one components, each corresponding to a specific polynomial feature. We further prove that the limiting large-dimensional and large sample training and test errors of the updated neural networks are fully characterized by these spikes. By precisely analyzing the improvement in the loss, we demonstrate that these non-linear features can enhance learning.

Large Language Models (LLMs) excel in NLP, but their demands hinder their widespread deployment. While Quantization-Aware Training (QAT) offers a solution, its extensive training costs make Post-Training Quantization (PTQ) a more practical approach for LLMs. In existing studies, activation outliers in particular channels are identified as the bottleneck to PTQ accuracy. They propose to transform the magnitudes from activations to weights, which however offers limited alleviation or suffers from unstable gradients, resulting in a severe performance drop at low-bitwidth. In this paper, we propose QLLM, an accurate and efficient low-bitwidth PTQ method designed for LLMs. QLLM introduces an adaptive channel reassembly technique that reallocates the magnitude of outliers to other channels, thereby mitigating their impact on the quantization range. This is achieved by channel disassembly and channel assembly, which first breaks down the outlier channels into several sub-channels to ensure a more balanced distribution of activation magnitudes. Then similar channels are merged to maintain the original channel number for efficiency. Additionally, an adaptive strategy is designed to autonomously determine the optimal number of sub-channels for channel disassembly. To further compensate for the performance loss caused by quantization, we propose an efficient tuning method that only learns a small number of low-rank weights while freezing the pre-trained quantized model. After training, these low-rank parameters can be fused into the frozen weights without affecting inference. Extensive experiments on LLaMA-1 and LLaMA-2 show that QLLM can obtain accurate quantized models efficiently. For example, QLLM quantizes the 4-bit LLaMA-2-70B within 10 hours on a single A100-80G GPU, outperforming the previous state-of-the-art method by 7.89% on the average accuracy across five zero-shot tasks.  ( 3 min )

Continual learning (CL) has remained a persistent challenge for deep neural networks due to catastrophic forgetting (CF) of previously learned tasks. Several techniques such as weight regularization, experience rehearsal, and parameter isolation have been proposed to alleviate CF. Despite their relative success, these research directions have predominantly remained orthogonal and suffer from several shortcomings, while missing out on the advantages of competing strategies. On the contrary, the brain continually learns, accommodates, and transfers knowledge across tasks by simultaneously leveraging several neurophysiological processes, including neurogenesis, active forgetting, neuromodulation, metaplasticity, experience rehearsal, and context-dependent gating, rarely resulting in CF. Inspired by how the brain exploits multiple mechanisms concurrently, we propose TriRE, a novel CL paradigm that encompasses retaining the most prominent neurons for each task, revising and solidifying the extracted knowledge of current and past tasks, and actively promoting less active neurons for subsequent tasks through rewinding and relearning. Across CL settings, TriRE significantly reduces task interference and surpasses different CL approaches considered in isolation.  ( 2 min )

Evaluating the adversarial robustness of machine learning models using gradient-based attacks is challenging. In this work, we show that hyperparameter optimization can improve fast minimum-norm attacks by automating the selection of the loss function, the optimizer and the step-size scheduler, along with the corresponding hyperparameters. Our extensive evaluation involving several robust models demonstrates the improved efficacy of fast minimum-norm attacks when hyper-up with hyperparameter optimization. We release our open-source code at https://github.com/pralab/HO-FMN.  ( 2 min )

Graph property prediction tasks are important and numerous. While each task offers a small size of labeled examples, unlabeled graphs have been collected from various sources and at a large scale. A conventional approach is training a model with the unlabeled graphs on self-supervised tasks and then fine-tuning the model on the prediction tasks. However, the self-supervised task knowledge could not be aligned or sometimes conflicted with what the predictions needed. In this paper, we propose to extract the knowledge underlying the large set of unlabeled graphs as a specific set of useful data points to augment each property prediction model. We use a diffusion model to fully utilize the unlabeled graphs and design two new objectives to guide the model's denoising process with each task's labeled data to generate task-specific graph examples and their labels. Experiments demonstrate that our data-centric approach performs significantly better than fifteen existing various methods on fifteen tasks. The performance improvement brought by unlabeled data is visible as the generated labeled examples unlike the self-supervised learning.  ( 2 min )

Many real-world prediction tasks have outcome variables that have characteristic heavy-tail distributions. Examples include copies of books sold, auction prices of art pieces, demand for commodities in warehouses, etc. By learning heavy-tailed distributions, "big and rare" instances (e.g., the best-sellers) will have accurate predictions. Most existing approaches are not dedicated to learning heavy-tailed distribution; thus, they heavily under-predict such instances. To tackle this problem, we introduce Learning to Place (L2P), which exploits the pairwise relationships between instances for learning. In its training phase, L2P learns a pairwise preference classifier: is instance A > instance B? In its placing phase, L2P obtains a prediction by placing the new instance among the known instances. Based on its placement, the new instance is then assigned a value for its outcome variable. Experiments on real data show that L2P outperforms competing approaches in terms of accuracy and ability to reproduce heavy-tailed outcome distribution. In addition, L2P provides an interpretable model by placing each predicted instance in relation to its comparable neighbors. Interpretable models are highly desirable when lives and treasure are at stake.

Predicting the docking between proteins and ligands is a crucial and challenging task for drug discovery. However, traditional docking methods mainly rely on scoring functions, and deep learning-based docking approaches usually neglect the 3D spatial information of proteins and ligands, as well as the graph-level features of ligands, which limits their performance. To address these limitations, we propose an equivariant transformer neural network for protein-ligand docking pose prediction. Our approach involves the fusion of ligand graph-level features by feature processing, followed by the learning of ligand and protein representations using our proposed TAMformer module. Additionally, we employ an iterative optimization approach based on the predicted distance matrix to generate refined ligand poses. The experimental results on real datasets show that our model can achieve state-of-the-art performance.  ( 2 min )

Aiming to build foundation models for time-series forecasting and study their scaling behavior, we present here our work-in-progress on Lag-Llama, a general-purpose univariate probabilistic time-series forecasting model trained on a large collection of time-series data. The model shows good zero-shot prediction capabilities on unseen "out-of-distribution" time-series datasets, outperforming supervised baselines. We use smoothly broken power-laws to fit and predict model scaling behavior. The open source code is made available at https://github.com/kashif/pytorch-transformer-ts.

Industrial systems such as recommender systems and online advertising, have been widely equipped with multi-stage architectures, which are divided into several cascaded modules, including matching, pre-ranking, ranking and re-ranking. As a critical bridge between matching and ranking, existing pre-ranking approaches mainly endure sample selection bias (SSB) problem owing to ignoring the entire-chain data dependence, resulting in sub-optimal performances. In this paper, we rethink pre-ranking system from the perspective of the entire sample space, and propose Entire-chain Cross-domain Models (ECM), which leverage samples from the whole cascaded stages to effectively alleviate SSB problem. Besides, we design a fine-grained neural structure named ECMM to further improve the pre-ranking accuracy. Specifically, we propose a cross-domain multi-tower neural network to comprehensively predict for each stage result, and introduce the sub-networking routing strategy with $L0$ regularization to reduce computational costs. Evaluations on real-world large-scale traffic logs demonstrate that our pre-ranking models outperform SOTA methods while time consumption is maintained within an acceptable level, which achieves better trade-off between efficiency and effectiveness.

Building agents based on tree-search planning capabilities with learned models has achieved remarkable success in classic decision-making problems, such as Go and Atari. However, it has been deemed challenging or even infeasible to extend Monte Carlo Tree Search (MCTS) based algorithms to diverse real-world applications, especially when these environments involve complex action spaces and significant simulation costs, or inherent stochasticity. In this work, we introduce LightZero, the first unified benchmark for deploying MCTS/MuZero in general sequential decision scenarios. Specificially, we summarize the most critical challenges in designing a general MCTS-style decision-making solver, then decompose the tightly-coupled algorithm and system design of tree-search RL methods into distinct sub-modules. By incorporating more appropriate exploration and optimization strategies, we can significantly enhance these sub-modules and construct powerful LightZero agents to tackle tasks across a wide range of domains, such as board games, Atari, MuJoCo, MiniGrid and GoBigger. Detailed benchmark results reveal the significant potential of such methods in building scalable and efficient decision intelligence. The code is available as part of OpenDILab at https://github.com/opendilab/LightZero.  ( 2 min )

Membership Inference Attacks (MIAs) aim to identify specific data samples within the private training dataset of machine learning models, leading to serious privacy violations and other sophisticated threats. Many practical black-box MIAs require query access to the data distribution (the same distribution where the private data is drawn) to train shadow models. By doing so, the adversary obtains models trained "with" or "without" samples drawn from the distribution, and analyzes the characteristics of the samples under consideration. The adversary is often required to train more than hundreds of shadow models to extract the signals needed for MIAs; this becomes the computational overhead of MIAs. In this paper, we propose that by strategically choosing the samples, MI adversaries can maximize their attack success while minimizing the number of shadow models. First, our motivational experiments suggest memorization as the key property explaining disparate sample vulnerability to MIAs. We formalize this through a theoretical bound that connects MI advantage with memorization. Second, we show sample complexity bounds that connect the number of shadow models needed for MIAs with memorization. Lastly, we confirm our theoretical arguments with comprehensive experiments; by utilizing samples with high memorization scores, the adversary can (a) significantly improve its efficacy regardless of the MIA used, and (b) reduce the number of shadow models by nearly two orders of magnitude compared to state-of-the-art approaches.  ( 2 min )

Although Maxwell discovered the physical laws of electromagnetic waves 160 years ago, how to precisely model the propagation of an RF signal in an electrically large and complex environment remains a long-standing problem. The difficulty is in the complex interactions between the RF signal and the obstacles (e.g., reflection, diffraction, etc.). Inspired by the great success of using a neural network to describe the optical field in computer vision, we propose a neural radio-frequency radiance field, NeRF$^\textbf{2}$, which represents a continuous volumetric scene function that makes sense of an RF signal's propagation. Particularly, after training with a few signal measurements, NeRF$^\textbf{2}$ can tell how/what signal is received at any position when it knows the position of a transmitter. As a physical-layer neural network, NeRF$^\textbf{2}$ can take advantage of the learned statistic model plus the physical model of ray tracing to generate a synthetic dataset that meets the training demands of application-layer artificial neural networks (ANNs). Thus, we can boost the performance of ANNs by the proposed turbo-learning, which mixes the true and synthetic datasets to intensify the training. Our experiment results show that turbo-learning can enhance performance with an approximate 50% increase. We also demonstrate the power of NeRF$^\textbf{2}$ in the field of indoor localization and 5G MIMO.

Markov Chain Monte Carlo methods for sampling from complex distributions and estimating normalization constants often simulate samples from a sequence of intermediate distributions along an annealing path, which bridges between a tractable initial distribution and a target density of interest. Prior work has constructed annealing paths using quasi-arithmetic means, and interpreted the resulting intermediate densities as minimizing an expected divergence to the endpoints. We provide a comprehensive analysis of this 'centroid' property using Bregman divergences under a monotonic embedding of the density function, thereby associating common divergences such as Amari's and Renyi's ${\alpha}$-divergences, ${(\alpha,\beta)}$-divergences, and the Jensen-Shannon divergence with intermediate densities along an annealing path. Our analysis highlights the interplay between parametric families, quasi-arithmetic means, and divergence functions using the rho-tau Bregman divergence framework of Zhang 2004,2013.

Practical real world datasets with plentiful categories introduce new challenges for unsupervised domain adaptation like small inter-class discriminability, that existing approaches relying on domain invariance alone cannot handle sufficiently well. In this work we propose MemSAC, which exploits sample level similarity across source and target domains to achieve discriminative transfer, along with architectures that scale to a large number of categories. For this purpose, we first introduce a memory augmented approach to efficiently extract pairwise similarity relations between labeled source and unlabeled target domain instances, suited to handle an arbitrary number of classes. Next, we propose and theoretically justify a novel variant of the contrastive loss to promote local consistency among within-class cross domain samples while enforcing separation between classes, thus preserving discriminative transfer from source to target. We validate the advantages of MemSAC with significant improvements over previous state-of-the-art on multiple challenging transfer tasks designed for large-scale adaptation, such as DomainNet with 345 classes and fine-grained adaptation on Caltech-UCSD birds dataset with 200 classes. We also provide in-depth analysis and insights into the effectiveness of MemSAC.

This paper studies the fundamental problem of learning multi-layer generator models. The multi-layer generator model builds multiple layers of latent variables as a prior model on top of the generator, which benefits learning complex data distribution and hierarchical representations. However, such a prior model usually focuses on modeling inter-layer relations between latent variables by assuming non-informative (conditional) Gaussian distributions, which can be limited in model expressivity. To tackle this issue and learn more expressive prior models, we propose an energy-based model (EBM) on the joint latent space over all layers of latent variables with the multi-layer generator as its backbone. Such joint latent space EBM prior model captures the intra-layer contextual relations at each layer through layer-wise energy terms, and latent variables across different layers are jointly corrected. We develop a joint training scheme via maximum likelihood estimation (MLE), which involves Markov Chain Monte Carlo (MCMC) sampling for both prior and posterior distributions of the latent variables from different layers. To ensure efficient inference and learning, we further propose a variational training scheme where an inference model is used to amortize the costly posterior MCMC sampling. Our experiments demonstrate that the learned model can be expressive in generating high-quality images and capturing hierarchical features for better outlier detection.  ( 2 min )

This paper presents a method to efficiently classify the gastroenterologic section of images derived from Video Capsule Endoscopy (VCE) studies by exploring the combination of a Convolutional Neural Network (CNN) for classification with the time-series analysis properties of a Hidden Markov Model (HMM). It is demonstrated that successive time-series analysis identifies and corrects errors in the CNN output. Our approach achieves an accuracy of $98.04\%$ on the Rhode Island (RI) Gastroenterology dataset. This allows for precise localization within the gastrointestinal (GI) tract while requiring only approximately 1M parameters and thus, provides a method suitable for low power devices  ( 2 min )

Machine learning models are often personalized with information that is protected, sensitive, self-reported, or costly to acquire. These models use information about people but do not facilitate nor inform their consent. Individuals cannot opt out of reporting personal information to a model, nor tell if they benefit from personalization in the first place. We introduce a family of classification models, called participatory systems, that let individuals opt into personalization at prediction time. We present a model-agnostic algorithm to learn participatory systems for personalization with categorical group attributes. We conduct a comprehensive empirical study of participatory systems in clinical prediction tasks, benchmarking them with common approaches for personalization and imputation. Our results demonstrate that participatory systems can facilitate and inform consent while improving performance and data use across all groups who report personal data.

Hyperdimensional computing (HDC) is a method to perform classification that uses binary vectors with high dimensions and the majority rule. This approach has the potential to be energy-efficient and hence deemed suitable for resource-limited platforms due to its simplicity and massive parallelism. However, in order to achieve high accuracy, HDC sometimes uses hypervectors with tens of thousands of dimensions. This potentially negates its efficiency advantage. In this paper, we examine the necessity of such high dimensions and conduct a detailed theoretical analysis of the relationship between hypervector dimensions and accuracy. Our results demonstrate that as the dimension of the hypervectors increases, the worst-case/average-case HDC prediction accuracy with the majority rule decreases. Building on this insight, we develop HDC models that use binary hypervectors with dimensions orders of magnitude lower than those of state-of-the-art HDC models while maintaining equivalent or even improved accuracy and efficiency. For instance, on the MNIST dataset, we achieve 91.12% HDC accuracy in image classification with a dimension of only 64. Our methods perform operations that are only 0.35% of other HDC models with dimensions of 10,000. Furthermore, we evaluate our methods on ISOLET, UCI-HAR, and Fashion-MNIST datasets and investigate the limits of HDC computing.  ( 2 min )

Natural Language Processing (NLP) has undergone transformative changes with the advent of deep learning methodologies. One challenge persistently confronting researchers is the scarcity of high-quality, annotated datasets that drive these models. This paper explores the nuances of synthetic data generation in NLP, with a focal point on template-based question generation. By assessing its advantages, including data augmentation potential and the introduction of structured variety, we juxtapose these benefits against inherent limitations, such as the risk of overfitting and the constraints posed by pre-defined templates. Drawing from empirical evaluations, we demonstrate the impact of template-based synthetic data on the performance of modern transformer models. We conclude by emphasizing the delicate balance required between synthetic and real-world data, and the future trajectories of integrating synthetic data in model training pipelines. The findings aim to guide NLP practitioners in harnessing synthetic data's potential, ensuring optimal model performance in diverse applications.  ( 2 min )

The classification of galaxy morphologies is an important step in the investigation of theories of hierarchical structure formation. While human expert visual classification remains quite effective and accurate, it cannot keep up with the massive influx of data from emerging sky surveys. A variety of approaches have been proposed to classify large numbers of galaxies; these approaches include crowdsourced visual classification, and automated and computational methods, such as machine learning methods based on designed morphology statistics and deep learning. In this work, we develop two novel galaxy morphology statistics, descent average and descent variance, which can be efficiently extracted from telescope galaxy images. We further propose simplified versions of the existing image statistics concentration, asymmetry, and clumpiness, which have been widely used in the literature of galaxy morphologies. We utilize the galaxy image data from the Sloan Digital Sky Survey to demonstrate the effective performance of our proposed image statistics at accurately detecting spiral and elliptical galaxies when used as features of a random forest classifier.  ( 2 min )

Multi-modal trajectory forecasting methods commonly evaluate using single-agent metrics (marginal metrics), such as minimum Average Displacement Error (ADE) and Final Displacement Error (FDE), which fail to capture joint performance of multiple interacting agents. Only focusing on marginal metrics can lead to unnatural predictions, such as colliding trajectories or diverging trajectories for people who are clearly walking together as a group. Consequently, methods optimized for marginal metrics lead to overly-optimistic estimations of performance, which is detrimental to progress in trajectory forecasting research. In response to the limitations of marginal metrics, we present the first comprehensive evaluation of state-of-the-art (SOTA) trajectory forecasting methods with respect to multi-agent metrics (joint metrics): JADE, JFDE, and collision rate. We demonstrate the importance of joint metrics as opposed to marginal metrics with quantitative evidence and qualitative examples drawn from the ETH / UCY and Stanford Drone datasets. We introduce a new loss function incorporating joint metrics that, when applied to a SOTA trajectory forecasting method, achieves a 7\% improvement in JADE / JFDE on the ETH / UCY datasets with respect to the previous SOTA. Our results also indicate that optimizing for joint metrics naturally leads to an improvement in interaction modeling, as evidenced by a 16\% decrease in mean collision rate on the ETH / UCY datasets with respect to the previous SOTA. Code is available at \texttt{\hyperlink{https://github.com/ericaweng/joint-metrics-matter}{github.com/ericaweng/joint-metrics-matter}}.  ( 3 min )

Content Delivery Networks carry the majority of Internet traffic, and the increasing demand for video content as a major IP traffic across the Internet highlights the importance of caching and prefetching optimization algorithms. Prefetching aims to make data available in the cache before the requester places its request to reduce access time and improve the Quality of Experience on the user side. Prefetching is well investigated in operating systems, compiler instructions, in-memory cache, local storage systems, high-speed networks, and cloud systems. Traditional prefetching techniques are well adapted to a particular access pattern, but fail to adapt to sudden variations or randomization in workloads. This paper explores the use of reinforcement learning to tackle the changes in user access patterns and automatically adapt over time. To this end, we propose, DeePref, a Deep Reinforcement Learning agent for online video content prefetching in Content Delivery Networks. DeePref is a prefetcher implemented on edge networks and is agnostic to hardware design, operating systems, and applications. Our results show that DeePref DRQN, using a real-world dataset, achieves a 17% increase in prefetching accuracy and a 28% increase in prefetching coverage on average compared to baseline approaches that use video content popularity as a building block to statically or dynamically make prefetching decisions. We also study the possibility of transfer learning of statistical models from one edge network into another, where unseen user requests from unknown distribution are observed. In terms of transfer learning, the increase in prefetching accuracy and prefetching coverage are [$30%$, $10%$], respectively. Our source code will be available on Github.  ( 3 min )

Real-world applications of contextual bandits often exhibit non-stationarity due to seasonality, serendipity, and evolving social trends. While a number of non-stationary contextual bandit learning algorithms have been proposed in the literature, they excessively explore due to a lack of prioritization for information of enduring value, or are designed in ways that do not scale in modern applications with high-dimensional user-specific features and large action set, or both. In this paper, we introduce a novel non-stationary contextual bandit algorithm that addresses these concerns. It combines a scalable, deep-neural-network-based architecture with a carefully designed exploration mechanism that strategically prioritizes collecting information with the most lasting value in a non-stationary environment. Through empirical evaluations on two real-world recommendation datasets, which exhibit pronounced non-stationarity, we demonstrate that our approach significantly outperforms the state-of-the-art baselines.  ( 2 min )

Traffic flow prediction is one of the most fundamental tasks of intelligent transportation systems. The complex and dynamic spatial-temporal dependencies make the traffic flow prediction quite challenging. Although existing spatial-temporal graph neural networks hold prominent, they often encounter challenges such as (1) ignoring the fixed graph that limits the predictive performance of the model, (2) insufficiently capturing complex spatial-temporal dependencies simultaneously, and (3) lacking attention to spatial-temporal information at different time lengths. In this paper, we propose a Multi-Scale Spatial-Temporal Recurrent Network for traffic flow prediction, namely MSSTRN, which consists of two different recurrent neural networks: the single-step gate recurrent unit and the multi-step gate recurrent unit to fully capture the complex spatial-temporal information in the traffic data under different time windows. Moreover, we propose a spatial-temporal synchronous attention mechanism that integrates adaptive position graph convolutions into the self-attention mechanism to achieve synchronous capture of spatial-temporal dependencies. We conducted extensive experiments on four real traffic datasets and demonstrated that our model achieves the best prediction accuracy with non-trivial margins compared to all the twenty baseline methods.  ( 2 min )

In 1-bit compressed sensing, the aim is to estimate a $k$-sparse unit vector $x\in S^{n-1}$ within an $\epsilon$ error (in $\ell_2$) from minimal number of linear measurements that are quantized to just their signs, i.e., from measurements of the form $y = \mathrm{Sign}(\langle a, x\rangle).$ In this paper, we study a noisy version where a fraction of the measurements can be flipped, potentially by an adversary. In particular, we analyze the Binary Iterative Hard Thresholding (BIHT) algorithm, a proximal gradient descent on a properly defined loss function used for 1-bit compressed sensing, in this noisy setting. It is known from recent results that, with $\tilde{O}(\frac{k}{\epsilon})$ noiseless measurements, BIHT provides an estimate within $\epsilon$ error. This result is optimal and universal, meaning one set of measurements work for all sparse vectors. In this paper, we show that BIHT also provides better results than all known methods for the noisy setting. We show that when up to $\tau$-fraction of the sign measurements are incorrect (adversarial error), with the same number of measurements as before, BIHT agnostically provides an estimate of $x$ within an $\tilde{O}(\epsilon+\tau)$ error, maintaining the universality of measurements. This establishes stability of iterative hard thresholding in the presence of measurement error. To obtain the result, we use the restricted approximate invertibility of Gaussian matrices, as well as a tight analysis of the high-dimensional geometry of the adversarially corrupted measurements.  ( 3 min )

Bayesian Optimization (BO) is typically used to optimize an unknown function $f$ that is noisy and costly to evaluate, by exploiting an acquisition function that must be maximized at each optimization step. Even if provably asymptotically optimal BO algorithms are efficient at optimizing low-dimensional functions, scaling them to high-dimensional spaces remains an open problem, often tackled by assuming an additive structure for $f$. By doing so, BO algorithms typically introduce additional restrictive assumptions on the additive structure that reduce their applicability domain. This paper contains two main contributions: (i) we relax the restrictive assumptions on the additive structure of $f$, at the expense of weakening the maximization guarantees of the acquisition function, and (ii) we address the over-exploration problem for decentralized BO algorithms. To these ends, we propose DumBO, an asymptotically optimal decentralized BO algorithm that achieves very competitive performance against state-of-the-art BO algorithms, especially when the additive structure of $f$ comprises high-dimensional factors.  ( 2 min )

Among recent developments in time series forecasting methods, deep forecasting models have gained popularity as they can utilize hidden feature patterns in time series to improve forecasting performance. Nevertheless, the majority of current deep forecasting models are opaque, hence making it challenging to interpret the results. While counterfactual explanations have been extensively employed as a post-hoc approach for explaining classification models, their application to forecasting models still remains underexplored. In this paper, we formulate the novel problem of counterfactual generation for time series forecasting, and propose an algorithm, called ForecastCF, that solves the problem by applying gradient-based perturbations to the original time series. ForecastCF guides the perturbations by applying constraints to the forecasted values to obtain desired prediction outcomes. We experimentally evaluate ForecastCF using four state-of-the-art deep model architectures and compare to two baselines. Our results show that ForecastCF outperforms the baseline in terms of counterfactual validity and data manifold closeness. Overall, our findings suggest that ForecastCF can generate meaningful and relevant counterfactual explanations for various forecasting tasks.  ( 2 min )

Semantic identifier (ID) is an important concept in information retrieval that aims to preserve the semantics of objects such as documents and items inside their IDs. Previous studies typically adopt a two-stage pipeline to learn semantic IDs by first procuring embeddings using off-the-shelf text encoders and then deriving IDs based on the embeddings. However, each step introduces potential information loss and there is usually an inherent mismatch between the distribution of embeddings within the latent space produced by text encoders and the anticipated distribution required for semantic indexing. Nevertheless, it is non-trivial to design a method that can learn the document's semantic representations and its hierarchical structure simultaneously, given that semantic IDs are discrete and sequentially structured, and the semantic supervision is deficient. In this paper, we introduce LMINDEXER, a self-supervised framework to learn semantic IDs with a generative language model. We tackle the challenge of sequential discrete ID by introducing a semantic indexer capable of generating neural sequential discrete representations with progressive training and contrastive learning. In response to the semantic supervision deficiency, we propose to train the model with a self-supervised document reconstruction objective. The learned semantic indexer can facilitate various downstream tasks, such as recommendation and retrieval. We conduct experiments on three tasks including recommendation, product search, and document retrieval on five datasets from various domains, where LMINDEXER outperforms competitive baselines significantly and consistently.

Federated learning allows multiple parties to collaboratively train a joint model without sharing local data. This enables applications of machine learning in settings of inherently distributed, undisclosable data such as in the medical domain. In practice, joint training is usually achieved by aggregating local models, for which local training objectives have to be in expectation similar to the joint (global) objective. Often, however, local datasets are so small that local objectives differ greatly from the global objective, resulting in federated learning to fail. We propose a novel approach that intertwines model aggregations with permutations of local models. The permutations expose each local model to a daisy chain of local datasets resulting in more efficient training in data-sparse domains. This enables training on extremely small local datasets, such as patient data across hospitals, while retaining the training efficiency and privacy benefits of federated learning.

Denoising diffusion models enable conditional generation and density modeling of complex relationships like images and text. However, the nature of the learned relationships is opaque making it difficult to understand precisely what relationships between words and parts of an image are captured, or to predict the effect of an intervention. We illuminate the fine-grained relationships learned by diffusion models by noticing a precise relationship between diffusion and information decomposition. Exact expressions for mutual information and conditional mutual information can be written in terms of the denoising model. Furthermore, pointwise estimates can be easily estimated as well, allowing us to ask questions about the relationships between specific images and captions. Decomposing information even further to understand which variables in a high-dimensional space carry information is a long-standing problem. For diffusion models, we show that a natural non-negative decomposition of mutual information emerges, allowing us to quantify informative relationships between words and pixels in an image. We exploit these new relations to measure the compositional understanding of diffusion models, to do unsupervised localization of objects in images, and to measure effects when selectively editing images through prompt interventions.

Public and private actors struggle to assess the vast amounts of information about sustainability commitments made by various institutions. To address this problem, we create a novel tool for automatically detecting corporate, national, and regional net zero and reduction targets in three steps. First, we introduce an expert-annotated data set with 3.5K text samples. Second, we train and release ClimateBERT-NetZero, a natural language classifier to detect whether a text contains a net zero or reduction target. Third, we showcase its analysis potential with two use cases: We first demonstrate how ClimateBERT-NetZero can be combined with conventional question-answering (Q&A) models to analyze the ambitions displayed in net zero and reduction targets. Furthermore, we employ the ClimateBERT-NetZero model on quarterly earning call transcripts and outline how communication patterns evolve over time. Our experiments demonstrate promising pathways for extracting and analyzing net zero and emission reduction targets at scale.

Recent research works have proposed machine learning models for classifying IoT devices connected to a network. However, there is still a practical challenge of not having all devices (and hence their traffic) available during the training of a model. This essentially means, during the operational phase, we need to classify new devices not seen during the training phase. To address this challenge, we propose ZEST -- a ZSL (zero-shot learning) framework based on self-attention for classifying both seen and unseen devices. ZEST consists of i) a self-attention based network feature extractor, termed SANE, for extracting latent space representations of IoT traffic, ii) a generative model that trains a decoder using latent features to generate pseudo data, and iii) a supervised model that is trained on the generated pseudo data for classifying devices. We carry out extensive experiments on real IoT traffic data; our experiments demonstrate i) ZEST achieves significant improvement (in terms of accuracy) over the baselines; ii) ZEST is able to better extract meaningful representations than LSTM which has been commonly used for modeling network traffic.  ( 2 min )

Leveraging nearest neighbor retrieval for self-supervised representation learning has proven beneficial with object-centric images. However, this approach faces limitations when applied to scene-centric datasets, where multiple objects within an image are only implicitly captured in the global representation. Such global bootstrapping can lead to undesirable entanglement of object representations. Furthermore, even object-centric datasets stand to benefit from a finer-grained bootstrapping approach. In response to these challenges, we introduce a novel Cross-Image Object-Level Bootstrapping method tailored to enhance dense visual representation learning. By employing object-level nearest neighbor bootstrapping throughout the training, CrIBo emerges as a notably strong and adequate candidate for in-context learning, leveraging nearest neighbor retrieval at test time. CrIBo shows state-of-the-art performance on the latter task while being highly competitive in more standard downstream segmentation tasks. Our code and pretrained models will be publicly available upon acceptance.  ( 2 min )

A major obstacle when attempting to train a machine learning system to evaluate facial clefts is the scarcity of large datasets of high-quality, ethics board-approved patient images. In response, we have built a deep learning-based cleft lip generator designed to produce an almost unlimited number of artificial images exhibiting high-fidelity facsimiles of cleft lip with wide variation. We undertook a transfer learning protocol testing different versions of StyleGAN-ADA (a generative adversarial network image generator incorporating adaptive data augmentation (ADA)) as the base model. Training images depicting a variety of cleft deformities were pre-processed to adjust for rotation, scaling, color adjustment and background blurring. The ADA modification of the primary algorithm permitted construction of our new generative model while requiring input of a relatively small number of training images. Adversarial training was carried out using 514 unique frontal photographs of cleft-affected faces to adapt a pre-trained model based on 70,000 normal faces. The Frechet Inception Distance (FID) was used to measure the similarity of the newly generated facial images to the cleft training dataset, while Perceptual Path Length (PPL) and the novel Divergence Index of Severity Histograms (DISH) measures were also used to assess the performance of the image generator that we dub CleftGAN. We found that StyleGAN3 with translation invariance (StyleGAN3-t) performed optimally as a base model. Generated images achieved a low FID reflecting a close similarity to our training input dataset of genuine cleft images. Low PPL and DISH measures reflected a smooth and semantically valid interpolation of images through the transfer learning process and a similar distribution of severity in the training and generated images, respectively.  ( 3 min )

Model identification of battery dynamics is a central problem in energy research; many energy management systems and design processes rely on accurate battery models for efficiency optimization. The standard methodology for battery modelling is traditional design of experiments (DoE), where the battery dynamics are excited with many different current profiles and the measured outputs are used to estimate the system dynamics. However, although it is possible to obtain useful models with the traditional approach, the process is time consuming and expensive because of the need to sweep many different current-profile configurations. In the present work, a novel DoE approach is developed based on deep reinforcement learning, which alters the configuration of the experiments on the fly based on the statistics of past experiments. Instead of sticking to a library of predefined current profiles, the proposed approach modifies the current profiles dynamically by updating the output space covered by past measurements, hence only the current profiles that are informative for future experiments are applied. Simulations and real experiments are used to show that the proposed approach gives models that are as accurate as those obtained with traditional DoE but by using 85\% less resources.  ( 2 min )

Researchers have long touted a vision of the future enabled by a proliferation of internet-of-things devices, including smart sensors, homes, and cities. Increasingly, embedding intelligence in such devices involves the use of deep neural networks. However, their storage and processing requirements make them prohibitive for cheap, off-the-shelf platforms. Overcoming those requirements is necessary for enabling widely-applicable smart devices. While many ways of making models smaller and more efficient have been developed, there is a lack of understanding of which ones are best suited for particular scenarios. More importantly for edge platforms, those choices cannot be analyzed in isolation from cost and user experience. In this work, we holistically explore how quantization, model scaling, and multi-modality interact with system components such as memory, sensors, and processors. We perform this hardware/software co-design from the cost, latency, and user-experience perspective, and develop a set of guidelines for optimal system design and model deployment for the most cost-constrained platforms. We demonstrate our approach using an end-to-end, on-device, biometric user authentication system using a $20 ESP-EYE board.  ( 2 min )

Few-shot object classification is the task of classifying objects in an image with limited number of examples as supervision. We propose a one-shot/few-shot classification model that can classify an object of any unseen class into a relatively general category in an hierarchically based classification. Our model uses a three-level hierarchical contrastive loss based ResNet152 classifier for classifying an object based on its features extracted from Image embedding, not used during the training phase. For our experimentation, we have used a subset of the ImageNet (ILSVRC-12) dataset that contains only the animal classes for training our model and created our own dataset of unseen classes for evaluating our trained model. Our model provides satisfactory results in classifying the unknown objects into a generic category which has been later discussed in greater detail.  ( 2 min )

We consider the problem of model selection in a high-dimensional sparse linear regression model under the differential privacy framework. In particular, we consider the problem of differentially private best subset selection and study its utility guarantee. We adopt the well-known exponential mechanism for selecting the best model, and under a certain margin condition, we establish its strong model recovery property. However, the exponential search space of the exponential mechanism poses a serious computational bottleneck. To overcome this challenge, we propose a Metropolis-Hastings algorithm for the sampling step and establish its polynomial mixing time to its stationary distribution in the problem parameters $n,p$, and $s$. Furthermore, we also establish approximate differential privacy for the final estimates of the Metropolis-Hastings random walk using its mixing property. Finally, we also perform some illustrative simulations that echo the theoretical findings of our main results.  ( 2 min )

Recent theoretical work has identified surprisingly simple reasoning problems, such as checking if two nodes in a graph are connected or simulating finite-state machines, that are provably unsolvable by standard transformers that answer immediately after reading their input. However, in practice, transformers' reasoning can be improved by allowing them to use a "chain of thought" or "scratchpad", i.e., generate and condition on a sequence of intermediate tokens before answering. Motivated by this, we ask: Does such intermediate generation fundamentally extend the computational power of a decoder-only transformer? We show that the answer is yes, but the amount of increase depends crucially on the amount of intermediate generation. For instance, we find that transformer decoders with a logarithmic number of decoding steps (w.r.t. the input length) push the limits of standard transformers only slightly, while a linear number of decoding steps adds a clear new ability (under standard complexity conjectures): recognizing all regular languages. Our results also imply that linear steps keep transformer decoders within context-sensitive languages, and polynomial steps make them recognize exactly the class of polynomial-time solvable problems -- the first exact characterization of a type of transformers in terms of standard complexity classes. Together, our results provide a nuanced framework for understanding how the length of a transformer's chain of thought or scratchpad impacts its reasoning power.  ( 2 min )

We propose a novel surrogate modelling approach to efficiently and accurately approximate the response of complex dynamical systems driven by time-varying exogenous excitations over extended time periods. Our approach, namely manifold nonlinear autoregressive modelling with exogenous input (mNARX), involves constructing a problem-specific exogenous input manifold that is optimal for constructing autoregressive surrogates. The manifold, which forms the core of mNARX, is constructed incrementally by incorporating the physics of the system, as well as prior expert- and domain- knowledge. Because mNARX decomposes the full problem into a series of smaller sub-problems, each with a lower complexity than the original, it scales well with the complexity of the problem, both in terms of training and evaluation costs of the final surrogate. Furthermore, mNARX synergizes well with traditional dimensionality reduction techniques, making it highly suitable for modelling dynamical systems with high-dimensional exogenous inputs, a class of problems that is typically challenging to solve. Since domain knowledge is particularly abundant in physical systems, such as those found in civil and mechanical engineering, mNARX is well suited for these applications. We demonstrate that mNARX outperforms traditional autoregressive surrogates in predicting the response of a classical coupled spring-mass system excited by a one-dimensional random excitation. Additionally, we show that mNARX is well suited for emulating very high-dimensional time- and state-dependent systems, even when affected by active controllers, by surrogating the dynamics of a realistic aero-servo-elastic onshore wind turbine simulator. In general, our results demonstrate that mNARX offers promising prospects for modelling complex dynamical systems, in terms of accuracy and efficiency.  ( 3 min )

The collective behavior of a network with heterogeneous, resource-limited information processing units (e.g., group of fish, flock of birds, or network of neurons) demonstrates high self-organization and complexity. These emergent properties arise from simple interaction rules where certain individuals can exhibit leadership-like behavior and influence the collective activity of the group. Motivated by the intricacy of these collectives, we propose a neural network (NN) architecture inspired by the rules observed in nature's collective ensembles. This NN structure contains workers that encompass one or more information processing units (e.g., neurons, filters, layers, or blocks of layers). Workers are either leaders or followers, and we train a leader-follower neural network (LFNN) by leveraging local error signals and optionally incorporating backpropagation (BP) and global loss. We investigate worker behavior and evaluate LFNNs through extensive experimentation. Our LFNNs trained with local error signals achieve significantly lower error rates than previous BP-free algorithms on MNIST and CIFAR-10 and even surpass BP-enabled baselines. In the case of ImageNet, our LFNN-l demonstrates superior scalability and outperforms previous BP-free algorithms by a significant margin.  ( 2 min )

Deep learning vulnerability detection has shown promising results in recent years. However, an important challenge that still blocks it from being very useful in practice is that the model is not robust under perturbation and it cannot generalize well over the out-of-distribution (OOD) data, e.g., applying a trained model to unseen projects in real world. We hypothesize that this is because the model learned non-robust features, e.g., variable names, that have spurious correlations with labels. When the perturbed and OOD datasets no longer have the same spurious features, the model prediction fails. To address the challenge, in this paper, we introduced causality into deep learning vulnerability detection. Our approach CausalVul consists of two phases. First, we designed novel perturbations to discover spurious features that the model may use to make predictions. Second, we applied the causal learning algorithms, specifically, do-calculus, on top of existing deep learning models to systematically remove the use of spurious features and thus promote causal based prediction. Our results show that CausalVul consistently improved the model accuracy, robustness and OOD performance for all the state-of-the-art models and datasets we experimented. To the best of our knowledge, this is the first work that introduces do calculus based causal learning to software engineering models and shows it's indeed useful for improving the model accuracy, robustness and generalization. Our replication package is located at https://figshare.com/s/0ffda320dcb96c249ef2.  ( 2 min )

Analytical theories suggest that higher-quality data can lead to lower test errors in models trained on a fixed data budget. Moreover, a model can be trained on a lower compute budget without compromising performance if a dataset can be stripped of its redundancies. Coreset selection (or data pruning) seeks to select a subset of the training data so as to maximize the performance of models trained on this subset, also referred to as coreset. There are two dominant approaches: (1) geometry-based data selection for maximizing data diversity in the coreset, and (2) functions that assign difficulty scores to samples based on training dynamics. Optimizing for data diversity leads to a coreset that is biased towards easier samples, whereas, selection by difficulty ranking omits easy samples that are necessary for the training of deep learning models. This demonstrates that data diversity and importance scores are two complementary factors that need to be jointly considered during coreset selection. We represent a dataset as an undirected graph and propose a novel pruning algorithm, D2 Pruning, that uses forward and reverse message passing over this dataset graph for coreset selection. D2 Pruning updates the difficulty scores of each example by incorporating the difficulty of its neighboring examples in the dataset graph. Then, these updated difficulty scores direct a graph-based sampling method to select a coreset that encapsulates both diverse and difficult regions of the dataset space. We evaluate supervised and self-supervised versions of our method on various vision and language datasets. Results show that D2 Pruning improves coreset selection over previous state-of-the-art methods for up to 70% pruning rates. Additionally, we find that using D2 Pruning for filtering large multimodal datasets leads to increased diversity in the dataset and improved generalization of pretrained models.  ( 3 min )

Randomized smoothing has recently attracted attentions in the field of adversarial robustness to provide provable robustness guarantees on smoothed neural network classifiers. However, existing works show that vanilla randomized smoothing usually does not provide good robustness performance and often requires (re)training techniques on the base classifier in order to boost the robustness of the resulting smoothed classifier. In this work, we propose two cost-effective approaches to boost the robustness of randomized smoothing while preserving its clean performance. The first approach introduces a new robust training method AdvMacerwhich combines adversarial training and robustness certification maximization for randomized smoothing. We show that AdvMacer can improve the robustness performance of randomized smoothing classifiers compared to SOTA baselines, while being 3x faster to train than MACER baseline. The second approach introduces a post-processing method EsbRS which greatly improves the robustness certificate based on building model ensembles. We explore different aspects of model ensembles that has not been studied by prior works and propose a novel design methodology to further improve robustness of the ensemble based on our theoretical analysis.  ( 2 min )

This paper proposes a set of novel optimization algorithms for solving a class of convex optimization problems with time-varying streaming cost function. We develop an approach to track the optimal solution with a bounded error. Unlike the existing results, our algorithm is executed only by using the first-order derivatives of the cost function which makes it computationally efficient for optimization with time-varying cost function. We compare our algorithms to the gradient descent algorithm and show why gradient descent is not an effective solution for optimization problems with time-varying cost. Several examples including solving a model predictive control problem cast as a convex optimization problem with a streaming time-varying cost function demonstrate our results.  ( 2 min )

The growing interest in machine learning problems over graphs with additional node information such as texts, images, or labels has popularized methods that require the costly operation of processing the entire graph. Yet, little effort has been made to the development of fast local methods (i.e. without accessing the entire graph) that extract useful information from such data. To that end, we propose a study of local graph clustering using noisy node labels as a proxy for additional node information. In this setting, nodes receive initial binary labels based on cluster affiliation: 1 if they belong to the target cluster and 0 otherwise. Subsequently, a fraction of these labels is flipped. We investigate the benefits of incorporating noisy labels for local graph clustering. By constructing a weighted graph with such labels, we study the performance of graph diffusion-based local clustering method on both the original and the weighted graphs. From a theoretical perspective, we consider recovering an unknown target cluster with a single seed node in a random graph with independent noisy node labels. We provide sufficient conditions on the label noise under which, with high probability, using diffusion in the weighted graph yields a more accurate recovery of the target cluster. This approach proves more effective than using the given labels alone or using diffusion in the label-free original graph. Empirically, we show that reliable node labels can be obtained with just a few samples from an attributed graph. Moreover, utilizing these labels via diffusion in the weighted graph leads to significantly better local clustering performance across several real-world datasets, improving F1 scores by up to 13%.  ( 3 min )

We investigate the problem of stochastic, combinatorial multi-armed bandits where the learner only has access to bandit feedback and the reward function can be non-linear. We provide a general framework for adapting discrete offline approximation algorithms into sublinear $\alpha$-regret methods that only require bandit feedback, achieving $\mathcal{O}\left(T^\frac{2}{3}\log(T)^\frac{1}{3}\right)$ expected cumulative $\alpha$-regret dependence on the horizon $T$. The framework only requires the offline algorithms to be robust to small errors in function evaluation. The adaptation procedure does not even require explicit knowledge of the offline approximation algorithm -- the offline algorithm can be used as a black box subroutine. To demonstrate the utility of the proposed framework, the proposed framework is applied to diverse applications in submodular maximization. The new CMAB algorithms for submodular maximization with knapsack constraints outperform a full-bandit method developed for the adversarial setting in experiments with real-world data.  ( 3 min )

We propose to use a simulation driven inverse inference approach to model the dynamics of tree branches under manipulation. Learning branch dynamics and gaining the ability to manipulate deformable vegetation can help with occlusion-prone tasks, such as fruit picking in dense foliage, as well as moving overhanging vines and branches for navigation in dense vegetation. The underlying deformable tree geometry is encapsulated as coarse spring abstractions executed on parallel, non-differentiable simulators. The implicit statistical model defined by the simulator, reference trajectories obtained by actively probing the ground truth, and the Bayesian formalism, together guide the spring parameter posterior density estimation. Our non-parametric inference algorithm, based on Stein Variational Gradient Descent, incorporates biologically motivated assumptions into the inference process as neural network driven learnt joint priors; moreover, it leverages the finite difference scheme for gradient approximations. Real and simulated experiments confirm that our model can predict deformation trajectories, quantify the estimation uncertainty, and it can perform better when base-lined against other inference algorithms, particularly from the Monte Carlo family. The model displays strong robustness properties in the presence of heteroscedastic sensor noise; furthermore, it can generalise to unseen grasp locations.  ( 2 min )

Due to the limited information about emerging diseases, symptoms are hard to be noticed and recognized, so that the window for clinical intervention could be ignored. An effective prognostic model is expected to assist doctors in making right diagnosis and designing personalized treatment plan, so to promptly prevent unfavorable outcomes. However, in the early stage of a disease, limited data collection and clinical experiences, plus the concern out of privacy and ethics, may result in restricted data availability for reference, to the extent that even data labels are difficult to mark correctly. In addition, Electronic Medical Record (EMR) data of different diseases or of different sources of the same disease can prove to be having serious cross-dataset feature misalignment problems, greatly mutilating the efficiency of deep learning models. This article introduces a transfer learning method to build a transition model from source dataset to target dataset. By way of constraining the distribution shift of features generated in disparate domains, domain-invariant features that are exclusively relative to downstream tasks are captured, so to cultivate a unified domain-invariant encoder across various task domains to achieve better feature representation. Experimental results of several target tasks demonstrate that our proposed model outperforms competing baseline methods and has higher rate of training convergence, especially in dealing with limited data amount. A multitude of experiences have proven the efficacy of our method to provide more accurate predictions concerning newly emergent pandemics and other diseases.  ( 3 min )

This paper introduces Elastic Decision Transformer (EDT), a significant advancement over the existing Decision Transformer (DT) and its variants. Although DT purports to generate an optimal trajectory, empirical evidence suggests it struggles with trajectory stitching, a process involving the generation of an optimal or near-optimal trajectory from the best parts of a set of sub-optimal trajectories. The proposed EDT differentiates itself by facilitating trajectory stitching during action inference at test time, achieved by adjusting the history length maintained in DT. Further, the EDT optimizes the trajectory by retaining a longer history when the previous trajectory is optimal and a shorter one when it is sub-optimal, enabling it to "stitch" with a more optimal trajectory. Extensive experimentation demonstrates EDT's ability to bridge the performance gap between DT-based and Q Learning-based approaches. In particular, the EDT outperforms Q Learning-based methods in a multi-task regime on the D4RL locomotion benchmark and Atari games. Videos are available at: https://kristery.github.io/edt/  ( 2 min )

Theoretical studies on transfer learning or domain adaptation have so far focused on situations with a known hypothesis class or model; however in practice, some amount of model selection is usually involved, often appearing under the umbrella term of hyperparameter-tuning: for example, one may think of the problem of tuning for the right neural network architecture towards a target task, while leveraging data from a related source task. Now, in addition to the usual tradeoffs on approximation vs estimation errors involved in model selection, this problem brings in a new complexity term, namely, the transfer distance between source and target distributions, which is known to vary with the choice of hypothesis class. We present a first study of this problem, focusing on classification; in particular, the analysis reveals some remarkable phenomena: adaptive rates, i.e., those achievable with no distributional information, can be arbitrarily slower than oracle rates, i.e., when given knowledge on distances.  ( 2 min )

We study the problem of building a controller that can follow open-ended instructions in open-world environments. We propose to follow reference videos as instructions, which offer expressive goal specifications while eliminating the need for expensive text-gameplay annotations. A new learning framework is derived to allow learning such instruction-following controllers from gameplay videos while producing a video instruction encoder that induces a structured goal space. We implement our agent GROOT in a simple yet effective encoder-decoder architecture based on causal transformers. We evaluate GROOT against open-world counterparts and human players on a proposed Minecraft SkillForge benchmark. The Elo ratings clearly show that GROOT is closing the human-machine gap as well as exhibiting a 70% winning rate over the best generalist agent baseline. Qualitative analysis of the induced goal space further demonstrates some interesting emergent properties, including the goal composition and complex gameplay behavior synthesis. Code and video can be found on the website https://craftjarvis-groot.github.io.  ( 2 min )

The lack of standardized robustness metrics and the widespread reliance on numerous unrelated benchmark datasets for testing have created a gap between academically validated robust models and their often problematic practical adoption. To address this, we introduce XIMAGENET-12, an explainable benchmark dataset with over 200K images and 15,600 manual semantic annotations. Covering 12 categories from ImageNet to represent objects commonly encountered in practical life and simulating six diverse scenarios, including overexposure, blurring, color changing, etc., we further propose a novel robustness criterion that extends beyond model generation ability assessment. This benchmark dataset, along with related code, is available at https://sites.google.com/view/ximagenet-12/home. Researchers and practitioners can leverage this resource to evaluate the robustness of their visual models under challenging conditions and ultimately benefit from the demands of practical computer vision systems.  ( 2 min )

Neural network pruning has shown to be an effective technique for reducing the network size, trading desirable properties like generalization and robustness to adversarial attacks for higher sparsity. Recent work has claimed that adversarial pruning methods can produce sparse networks while also preserving robustness to adversarial examples. In this work, we first re-evaluate three state-of-the-art adversarial pruning methods, showing that their robustness was indeed overestimated. We then compare pruned and dense versions of the same models, discovering that samples on thin ice, i.e., closer to the unpruned model's decision boundary, are typically misclassified after pruning. We conclude by discussing how this intuition may lead to designing more effective adversarial pruning methods in future work.  ( 2 min )

Multiscale modeling of complex systems is crucial for understanding their intricacies. Data-driven multiscale modeling has emerged as a promising approach to tackle challenges associated with complex systems. On the other hand, self-similarity is prevalent in complex systems, hinting that large-scale complex systems can be modeled at a reduced cost. In this paper, we introduce a multiscale neural network framework that incorporates self-similarity as prior knowledge, facilitating the modeling of self-similar dynamical systems. For deterministic dynamics, our framework can discern whether the dynamics are self-similar. For uncertain dynamics, it can compare and determine which parameter set is closer to self-similarity. The framework allows us to extract scale-invariant kernels from the dynamics for modeling at any scale. Moreover, our method can identify the power law exponents in self-similar systems. Preliminary tests on the Ising model yielded critical exponents consistent with theoretical expectations, providing valuable insights for addressing critical phase transitions in non-equilibrium systems.  ( 2 min )

Choosing an appropriate tokenization scheme is often a bottleneck in low-resource cross-lingual transfer. To understand the downstream implications of text representation choices, we perform a comparative analysis on language models having diverse text representation modalities including 2 segmentation-based models (\texttt{BERT}, \texttt{mBERT}), 1 image-based model (\texttt{PIXEL}), and 1 character-level model (\texttt{CANINE}). First, we propose a scoring Language Quotient (LQ) metric capable of providing a weighted representation of both zero-shot and few-shot evaluation combined. Utilizing this metric, we perform experiments comprising 19 source languages and 133 target languages on three tasks (POS tagging, Dependency parsing, and NER). Our analysis reveals that image-based models excel in cross-lingual transfer when languages are closely related and share visually similar scripts. However, for tasks biased toward word meaning (POS, NER), segmentation-based models prove to be superior. Furthermore, in dependency parsing tasks where word relationships play a crucial role, models with their character-level focus, outperform others. Finally, we propose a recommendation scheme based on our findings to guide model selection according to task and language requirements.  ( 2 min )

Learning from Label Proportions (LLP) is a learning problem where only aggregate level labels are available for groups of instances, called bags, during training, and the aim is to get the best performance at the instance-level on the test data. This setting arises in domains like advertising and medicine due to privacy considerations. We propose a novel algorithmic framework for this problem that iteratively performs two main steps. For the first step (Pseudo Labeling) in every iteration, we define a Gibbs distribution over binary instance labels that incorporates a) covariate information through the constraint that instances with similar covariates should have similar labels and b) the bag level aggregated label. We then use Belief Propagation (BP) to marginalize the Gibbs distribution to obtain pseudo labels. In the second step (Embedding Refinement), we use the pseudo labels to provide supervision for a learner that yields a better embedding. Further, we iterate on the two steps again by using the second step's embeddings as new covariates for the next iteration. In the final iteration, a classifier is trained using the pseudo labels. Our algorithm displays strong gains against several SOTA baselines (up to 15%) for the LLP Binary Classification problem on various dataset types - tabular and Image. We achieve these improvements with minimal computational overhead above standard supervised learning due to Belief Propagation, for large bag sizes, even for a million samples.  ( 2 min )

We review four types of algorithms for physics-informed machine learning (PIML) inversion of geophysical data. The unifying equation is given by the joint objective function $\epsilon$: \begin{eqnarray} \epsilon^{||-PIML}&=&\lambda_1 \overbrace{||{\bf W}^{ML}({\bf H}_{{\bf w}} {\bf d}^{obs}-{\bf m})||^2}^{NN} + \lambda_2 \overbrace{{||{\bf W}^{FWI}({\bf L} {\bf m}-{\bf d}^{obs})||^2}}^{FWI} ~+ \nonumber\\ \nonumber\\ && + ~~Regularizer, \label{PIML.eq120} \end{eqnarray}where the optimal model ${\bf m}^*$ and weights $\bf w^*$ minimize $\epsilon$. Here, The matrix weights are given by the boldface symbol $\bf W$, and full waveform inversion (FWI) is typically computed using a finite-difference solution of the wave equation, where $\bf L$ represents the forward modeling operation of the wave equation as a function of the model $\bf m$. Also, a fully-connected neural network (NN) is used to compute the model ${\bf H_w}{\bf d}^{obs} \approx \bf m$ from the observed input data ${\bf d}^{obs}$. The selection of weights $\lambda_i$ and the NN operations determine one of four different PIML algorithms. PIML offers potential advantages over standard FWI through its enhanced ability to avoid local minima and the option to locally train the inversion operator, minimizing the requirement for extensive training data for global applicability. However, the effectiveness of PIML relies on the similarity between the test and trained data. Nevertheless, a possible strategy to overcome this limitation involves initial pretraining of a PIML architecture with data from a broader region, followed by fine-tuning for specific data-a method reminiscent of the way large language models are pretrained and adapted for various tasks.  ( 2 min )

Brain-Computer Interfaces (BCIs) are a groundbreaking technology for interacting with external devices using brain signals. Despite advancements, electroencephalogram (EEG)-based Motor Imagery (MI) tasks face challenges like amplitude and phase variability, and complex spatial correlations, with a need for smaller model size and faster inference. This study introduces the LGL-BCI framework, employing a Geometric Deep Learning Framework for EEG processing in non-Euclidean metric spaces, particularly the Symmetric Positive Definite (SPD) Manifold space. LGL-BCI offers robust EEG data representation and captures spatial correlations. We propose an EEG channel selection solution via a feature decomposition algorithm to reduce SPD matrix dimensionality, with a lossless transformation boosting inference speed. Extensive experiments show LGL-BCI's superior accuracy and efficiency compared to current solutions, highlighting geometric deep learning's potential in MI-BCI applications. The efficiency, assessed on two public EEG datasets and two real-world EEG devices, significantly outperforms the state-of-the-art solution in accuracy ($82.54\%$ versus $62.22\%$) with fewer parameters (64.9M compared to 183.7M).  ( 2 min )

Keyphrase generation (KG) aims to generate a set of summarizing words or phrases given a source document, while keyphrase extraction (KE) aims to identify them from the text. Because the search space is much smaller in KE, it is often combined with KG to predict keyphrases that may or may not exist in the corresponding document. However, current unified approaches adopt sequence labeling and maximization-based generation that primarily operate at a token level, falling short in observing and scoring keyphrases as a whole. In this work, we propose SimCKP, a simple contrastive learning framework that consists of two stages: 1) An extractor-generator that extracts keyphrases by learning context-aware phrase-level representations in a contrastive manner while also generating keyphrases that do not appear in the document; 2) A reranker that adapts scores for each generated phrase by likewise aligning their representations with the corresponding document. Experimental results on multiple benchmark datasets demonstrate the effectiveness of our proposed approach, which outperforms the state-of-the-art models by a significant margin.  ( 2 min )

Existing literature on adversarial Machine Learning (ML) focuses either on showing attacks that break every ML model, or defenses that withstand most attacks. Unfortunately, little consideration is given to the actual feasibility of the attack or the defense. Moreover, adversarial samples are often crafted in the "feature-space", making the corresponding evaluations of questionable value. Simply put, the current situation does not allow to estimate the actual threat posed by adversarial attacks, leading to a lack of secure ML systems. We aim to clarify such confusion in this paper. By considering the application of ML for Phishing Website Detection (PWD), we formalize the "evasion-space" in which an adversarial perturbation can be introduced to fool a ML-PWD -- demonstrating that even perturbations in the "feature-space" are useful. Then, we propose a realistic threat model describing evasion attacks against ML-PWD that are cheap to stage, and hence intrinsically more attractive for real phishers. After that, we perform the first statistically validated assessment of state-of-the-art ML-PWD against 12 evasion attacks. Our evaluation shows (i) the true efficacy of evasion attempts that are more likely to occur; and (ii) the impact of perturbations crafted in different evasion-spaces. Our realistic evasion attempts induce a statistically significant degradation (3-10% at p<0.05), and their cheap cost makes them a subtle threat. Notably, however, some ML-PWD are immune to our most realistic attacks (p=0.22). Finally, as an additional contribution of this journal publication, we are the first to consider the intriguing case wherein an attacker introduces perturbations in multiple evasion-spaces at the same time. These new results show that simultaneously applying perturbations in the problem- and feature-space can cause a drop in the detection rate from 0.95 to 0.  ( 3 min )

Document-level relation extraction (DocRE) aims to extract relations of all entity pairs in a document. A key challenge in DocRE is the cost of annotating such data which requires intensive human effort. Thus, we investigate the case of DocRE in a low-resource setting, and we find that existing models trained on low data overestimate the NA ("no relation") label, causing limited performance. In this work, we approach the problem from a calibration perspective and propose PRiSM, which learns to adapt logits based on relation semantic information. We evaluate our method on three DocRE datasets and demonstrate that integrating existing models with PRiSM improves performance by as much as 26.38 F1 score, while the calibration error drops as much as 36 times when trained with about 3% of data. The code is publicly available at https://github.com/brightjade/PRiSM.  ( 2 min )

This paper considers the best policy identification (BPI) problem in online Constrained Markov Decision Processes (CMDPs). We are interested in algorithms that are model-free, have low regret, and identify an optimal policy with a high probability. Existing model-free algorithms for online CMDPs with sublinear regret and constraint violation do not provide any convergence guarantee to an optimal policy and provide only average performance guarantees when a policy is uniformly sampled at random from all previously used policies. In this paper, we develop a new algorithm, named Pruning-Refinement-Identification (PRI), based on a fundamental structural property of CMDPs we discover, called limited stochasticity. The property says for a CMDP with $N$ constraints, there exists an optimal policy with at most $N$ stochastic decisions. The proposed algorithm first identifies at which step and in which state a stochastic decision has to be taken and then fine-tunes the distributions of these stochastic decisions. PRI achieves trio objectives: (i) PRI is a model-free algorithm; and (ii) it outputs a near-optimal policy with a high probability at the end of learning; and (iii) in the tabular setting, PRI guarantees $\tilde{\mathcal{O}}(\sqrt{K})$ regret and constraint violation, which significantly improves the best existing regret bound $\tilde{\mathcal{O}}(K^{\frac{4}{5}})$ under a model-free algorithm, where $K$ is the total number of episodes.  ( 2 min )

With the success of large-scale pretraining in NLP, there is an increasing trend of applying it to the domain of life sciences. In particular, pretraining methods based on DNA sequences have garnered growing attention due to their potential to capture generic information about genes. However, existing pretraining methods for DNA sequences largely rely on direct adoptions of BERT pretraining from NLP, lacking a comprehensive understanding and a specifically tailored approach. To address this research gap, we first conducted a series of exploratory experiments and gained several insightful observations: 1) In the fine-tuning phase of downstream tasks, when using K-mer overlapping tokenization instead of K-mer non-overlapping tokenization, both overlapping and non-overlapping pretraining weights show consistent performance improvement.2) During the pre-training process, using K-mer overlapping tokenization quickly produces clear K-mer embeddings and reduces the loss to a very low level, while using K-mer non-overlapping tokenization results in less distinct embeddings and continuously decreases the loss. 3) Using overlapping tokenization causes the self-attention in the intermediate layers of pre-trained models to tend to overly focus on certain tokens, reflecting that these layers are not adequately optimized. In summary, overlapping tokenization can benefit the fine-tuning of downstream tasks but leads to inadequate pretraining with fast convergence. To unleash the pretraining potential, we introduce a novel approach called RandomMask, which gradually increases the task difficulty of BERT-like pretraining by continuously expanding its mask boundary, forcing the model to learn more knowledge. RandomMask is simple but effective, achieving top-tier performance across 26 datasets of 28 datasets spanning 7 downstream tasks.  ( 3 min )

Transformers have become the go-to architecture for language and vision tasks, yet their theoretical properties, especially memorization capacity, remain elusive. This paper investigates the memorization abilities of multi-head attention mechanisms, examining how many example sequences they can memorize, as a function of the number of heads and sequence length. Motivated by experimental findings on vision transformers, we introduce novel assumptions about the linear independence of input data, distinct from the commonly used general-position assumption. Under these assumptions, we demonstrate that an attention layer with $H$ heads, dimension $d$, and context size $n < d$, featuring $\Theta(Hd^2)$ parameters, can memorize $\Omega(Hn)$ examples. Our analysis sheds light on how different attention heads handle various example sequences, aided by the softmax operator's saturation property. We validate our findings through experiments on synthetic data.  ( 2 min )

Benchmarking is a common method for evaluating trajectory prediction models for autonomous driving. Existing benchmarks rely on datasets, which are biased towards more common scenarios, such as cruising, and distance-based metrics that are computed by averaging over all scenarios. Following such a regiment provides a little insight into the properties of the models both in terms of how well they can handle different scenarios and how admissible and diverse their outputs are. There exist a number of complementary metrics designed to measure the admissibility and diversity of trajectories, however, they suffer from biases, such as length of trajectories. In this paper, we propose a new benChmarking paRadIgm for evaluaTing trajEctoRy predIction Approaches (CRITERIA). Particularly, we propose 1) a method for extracting driving scenarios at varying levels of specificity according to the structure of the roads, models' performance, and data properties for fine-grained ranking of prediction models; 2) A set of new bias-free metrics for measuring diversity, by incorporating the characteristics of a given scenario, and admissibility, by considering the structure of roads and kinematic compliancy, motivated by real-world driving constraints. 3) Using the proposed benchmark, we conduct extensive experimentation on a representative set of the prediction models using the large scale Argoverse dataset. We show that the proposed benchmark can produce a more accurate ranking of the models and serve as a means of characterizing their behavior. We further present ablation studies to highlight contributions of different elements that are used to compute the proposed metrics.  ( 3 min )

Reinforcement learning in sparse-reward navigation environments with expensive and limited interactions is challenging and poses a need for effective exploration. Motivated by complex navigation tasks that require real-world training (when cheap simulators are not available), we consider an agent that faces an unknown distribution of environments and must decide on an exploration strategy. It may leverage a series of training environments to improve its policy before it is evaluated in a test environment drawn from the same environment distribution. Most existing approaches focus on fixed exploration strategies, while the few that view exploration as a meta-optimization problem tend to ignore the need for cost-efficient exploration. We propose a cost-aware Bayesian optimization approach that efficiently searches over a class of dynamic subgoal-based exploration strategies. The algorithm adjusts a variety of levers -- the locations of the subgoals, the length of each episode, and the number of replications per trial -- in order to overcome the challenges of sparse rewards, expensive interactions, and noise. An experimental evaluation demonstrates that the new approach outperforms existing baselines across a number of problem domains. We also provide a theoretical foundation and prove that the method asymptotically identifies a near-optimal subgoal design.  ( 2 min )

We present AIRS: Automatic Intrinsic Reward Shaping that intelligently and adaptively provides high-quality intrinsic rewards to enhance exploration in reinforcement learning (RL). More specifically, AIRS selects shaping function from a predefined set based on the estimated task return in real-time, providing reliable exploration incentives and alleviating the biased objective problem. Moreover, we develop an intrinsic reward toolkit to provide efficient and reliable implementations of diverse intrinsic reward approaches. We test AIRS on various tasks of MiniGrid, Procgen, and DeepMind Control Suite. Extensive simulation demonstrates that AIRS can outperform the benchmarking schemes and achieve superior performance with simple architecture.  ( 2 min )

We consider the problem of a revenue-maximizing seller with a large number of items $m$ for sale to $n$ strategic bidders, whose valuations are drawn independently from high-dimensional, unknown prior distributions. It is well-known that optimal and even approximately-optimal mechanisms for this setting are notoriously difficult to characterize or compute, and, even when they can be found, are often rife with various counter-intuitive properties. In this paper, following a model introduced recently by Cai and Daskalakis~\cite{cai2022recommender}, we consider the case that bidders' prior distributions can be well-approximated by a topic model. We design an active learning component, responsible for interacting with the bidders and outputting low-dimensional approximations of their types, and a mechanism design component, responsible for robustifying mechanisms for the low-dimensional model to work for the approximate types of the former component. On the active learning front, we cast our problem in the framework of Randomized Linear Algebra (RLA) for regression problems, allowing us to import several breakthrough results from that line of research, and adapt them to our setting. On the mechanism design front, we remove many restrictive assumptions of prior work on the type of access needed to the underlying distributions and the associated mechanisms. To the best of our knowledge, our work is the first to formulate connections between mechanism design, and RLA for active learning of regression problems, opening the door for further applications of randomized linear algebra primitives to mechanism design.  ( 3 min )

Current techniques for Out-of-Distribution (OoD) detection predominantly rely on quantifying predictive uncertainty and incorporating model regularization during the training phase, using either real or synthetic OoD samples. However, methods that utilize real OoD samples lack exploration and are prone to overfit the OoD samples at hand. Whereas synthetic samples are often generated based on features extracted from training data, rendering them less effective when the training and OoD data are highly overlapped in the feature space. In this work, we propose a Wasserstein-score-based generative adversarial training scheme to enhance OoD detection accuracy, which, for the first time, performs data augmentation and exploration simultaneously under the supervision of limited OoD samples. Specifically, the generator explores OoD spaces and generates synthetic OoD samples using feedback from the discriminator, while the discriminator exploits both the observed and synthesized samples for OoD detection using a predefined Wasserstein score. We provide theoretical guarantees that the optimal solutions of our generative scheme are statistically achievable through adversarial training in empirical settings. We then demonstrate that the proposed method outperforms state-of-the-art techniques on various computer vision datasets and exhibits superior generalizability to unseen OoD data.  ( 2 min )

Given an image and an associated textual question, the purpose of Knowledge-Based Visual Question Answering (KB-VQA) is to provide a correct answer to the question with the aid of external knowledge bases. Prior KB-VQA models are usually formulated as a retriever-classifier framework, where a pre-trained retriever extracts textual or visual information from knowledge graphs and then makes a prediction among the candidates. Despite promising progress, there are two drawbacks with existing models. Firstly, modeling question-answering as multi-class classification limits the answer space to a preset corpus and lacks the ability of flexible reasoning. Secondly, the classifier merely consider "what is the answer" without "how to get the answer", which cannot ground the answer to explicit reasoning paths. In this paper, we confront the challenge of \emph{explainable open-set} KB-VQA, where the system is required to answer questions with entities at wild and retain an explainable reasoning path. To resolve the aforementioned issues, we propose a new retriever-ranker paradigm of KB-VQA, Graph pATH rankER (GATHER for brevity). Specifically, it contains graph constructing, pruning, and path-level ranking, which not only retrieves accurate answers but also provides inference paths that explain the reasoning process. To comprehensively evaluate our model, we reformulate the benchmark dataset OK-VQA with manually corrected entity-level annotations and release it as ConceptVQA. Extensive experiments on real-world questions demonstrate that our framework is not only able to perform open-set question answering across the whole knowledge base but provide explicit reasoning path.  ( 2 min )

Existing regression models tend to fall short in both accuracy and uncertainty estimation when the label distribution is imbalanced. In this paper, we propose a probabilistic deep learning model, dubbed variational imbalanced regression (VIR), which not only performs well in imbalanced regression but naturally produces reasonable uncertainty estimation as a byproduct. Different from typical variational autoencoders assuming I.I.D. representations (a data point's representation is not directly affected by other data points), our VIR borrows data with similar regression labels to compute the latent representation's variational distribution; furthermore, different from deterministic regression models producing point estimates, VIR predicts the entire normal-inverse-gamma distributions and modulates the associated conjugate distributions to impose probabilistic reweighting on the imbalanced data, thereby providing better uncertainty estimation. Experiments in several real-world datasets show that our VIR can outperform state-of-the-art imbalanced regression models in terms of both accuracy and uncertainty estimation. Code will soon be available at \url{https://github.com/Wang-ML-Lab/variational-imbalanced-regression}.  ( 2 min )

This system paper presents a software framework for the support of topological analysis pipelines in a distributed-memory model. While several recent papers introduced topology-based approaches for distributed-memory environments, these were reporting experiments obtained with tailored, mono-algorithm implementations. In contrast, we describe in this paper a general-purpose, generic framework for topological analysis pipelines, i.e. a sequence of topological algorithms interacting together, possibly on distinct numbers of processes. Specifically, we instantiated our framework with the MPI model, within the Topology ToolKit (TTK). While developing this framework, we faced several algorithmic and software engineering challenges, which we document in this paper. We provide a taxonomy for the distributed-memory topological algorithms supported by TTK, depending on their communication needs and provide examples of hybrid MPI+thread parallelizations. Detailed performance analyses show that parallel efficiencies range from $20\%$ to $80\%$ (depending on the algorithms), and that the MPI-specific preconditioning introduced by our framework induces a negligible computation time overhead. We illustrate the new distributed-memory capabilities of TTK with an example of advanced analysis pipeline, combining multiple algorithms, run on the largest publicly available dataset we have found (120 billion vertices) on a standard cluster with 64 nodes (for a total of 1,536 cores). Finally, we provide a roadmap for the completion of TTK's MPI extension, along with generic recommendations for each algorithm communication category.  ( 3 min )

As machine learning technology gets applied to actual products and solutions, new challenges have emerged. Models unexpectedly fail to generalize to small changes in the distribution, tend to be confident on novel data they have never seen, or cannot communicate the rationale behind their decisions effectively with the end users. Collectively, we face a trustworthiness issue with the current machine learning technology. This textbook on Trustworthy Machine Learning (TML) covers a theoretical and technical background of four key topics in TML: Out-of-Distribution Generalization, Explainability, Uncertainty Quantification, and Evaluation of Trustworthiness. We discuss important classical and contemporary research papers of the aforementioned fields and uncover and connect their underlying intuitions. The book evolved from the homonymous course at the University of T\"ubingen, first offered in the Winter Semester of 2022/23. It is meant to be a stand-alone product accompanied by code snippets and various pointers to further sources on topics of TML. The dedicated website of the book is https://trustworthyml.io/.  ( 2 min )

Covariate shift occurs prevalently in practice, where the input distributions of the source and target data are substantially different. Despite its practical importance in various learning problems, most of the existing methods only focus on some specific learning tasks and are not well validated theoretically and numerically. To tackle this problem, we propose a unified analysis of general nonparametric methods in a reproducing kernel Hilbert space (RKHS) under covariate shift. Our theoretical results are established for a general loss belonging to a rich loss function family, which includes many commonly used methods as special cases, such as mean regression, quantile regression, likelihood-based classification, and margin-based classification. Two types of covariate shift problems are the focus of this paper and the sharp convergence rates are established for a general loss function to provide a unified theoretical analysis, which concurs with the optimal results in literature where the squared loss is used. Extensive numerical studies on synthetic and real examples confirm our theoretical findings and further illustrate the effectiveness of our proposed method.  ( 2 min )

Background: Missing data is a common challenge in mass spectrometry-based metabolomics, which can lead to biased and incomplete analyses. The integration of whole-genome sequencing (WGS) data with metabolomics data has emerged as a promising approach to enhance the accuracy of data imputation in metabolomics studies. Method: In this study, we propose a novel method that leverages the information from WGS data and reference metabolites to impute unknown metabolites. Our approach utilizes a multi-view variational autoencoder to jointly model the burden score, polygenetic risk score (PGS), and linkage disequilibrium (LD) pruned single nucleotide polymorphisms (SNPs) for feature extraction and missing metabolomics data imputation. By learning the latent representations of both omics data, our method can effectively impute missing metabolomics values based on genomic information. Results: We evaluate the performance of our method on empirical metabolomics datasets with missing values and demonstrate its superiority compared to conventional imputation techniques. Using 35 template metabolites derived burden scores, PGS and LD-pruned SNPs, the proposed methods achieved r2-scores > 0.01 for 71.55% of metabolites. Conclusion: The integration of WGS data in metabolomics imputation not only improves data completeness but also enhances downstream analyses, paving the way for more comprehensive and accurate investigations of metabolic pathways and disease associations. Our findings offer valuable insights into the potential benefits of utilizing WGS data for metabolomics data imputation and underscore the importance of leveraging multi-modal data integration in precision medicine research.  ( 3 min )

Diffusion models (DMs) represent state-of-the-art generative models for continuous inputs. DMs work by constructing a Stochastic Differential Equation (SDE) in the input space (ie, position space), and using a neural network to reverse it. In this work, we introduce a novel generative modeling framework grounded in \textbf{phase space dynamics}, where a phase space is defined as {an augmented space encompassing both position and velocity.} Leveraging insights from Stochastic Optimal Control, we construct a path measure in the phase space that enables efficient sampling. {In contrast to DMs, our framework demonstrates the capability to generate realistic data points at an early stage of dynamics propagation.} This early prediction sets the stage for efficient data generation by leveraging additional velocity information along the trajectory. On standard image generation benchmarks, our model yields favorable performance over baselines in the regime of small Number of Function Evaluations (NFEs). Furthermore, our approach rivals the performance of diffusion models equipped with efficient sampling techniques, underscoring its potential as a new tool generative modeling.  ( 2 min )

We aim to optimize the binary detection of Chronic Obstructive Pulmonary Disease (COPD) based on emphysema presence in the lung with convolutional neural networks (CNN) by exploring manually adjusted versus automated window-setting optimization (WSO) on computed tomography (CT) images. 7,194 CT images (3,597 with COPD; 3,597 healthy controls) from 78 subjects (43 with COPD; 35 healthy controls) were selected retrospectively (10.2018-12.2019) and preprocessed. For each image, intensity values were manually clipped to the emphysema window setting and a baseline 'full-range' window setting. Class-balanced train, validation, and test sets contained 3,392, 1,114, and 2,688 images. The network backbone was optimized by comparing various CNN architectures. Furthermore, automated WSO was implemented by adding a customized layer to the model. The image-level area under the Receiver Operating Characteristics curve (AUC) [lower, upper limit 95% confidence] was utilized to compare model variations. Repeated inference (n=7) on the test set showed that the DenseNet was the most efficient backbone and achieved a mean AUC of 0.80 [0.76, 0.85] without WSO. Comparably, with input images manually adjusted to the emphysema window, the DenseNet model predicted COPD with a mean AUC of 0.86 [0.82, 0.89]. By adding a customized WSO layer to the DenseNet, an optimal window in the proximity of the emphysema window setting was learned automatically, and a mean AUC of 0.82 [0.78, 0.86] was achieved. Detection of COPD with DenseNet models was improved by WSO of CT data to the emphysema window setting range.  ( 3 min )

Image-level weakly-supervised semantic segmentation (WSSS) reduces the usually vast data annotation cost by surrogate segmentation masks during training. The typical approach involves training an image classification network using global average pooling (GAP) on convolutional feature maps. This enables the estimation of object locations based on class activation maps (CAMs), which identify the importance of image regions. The CAMs are then used to generate pseudo-labels, in the form of segmentation masks, to supervise a segmentation model in the absence of pixel-level ground truth. Our work is based on two techniques for improving CAMs; importance sampling, which is a substitute for GAP, and the feature similarity loss, which utilizes a heuristic that object contours almost always align with color edges in images. However, both are based on the multinomial posterior with softmax, and implicitly assume that classes are mutually exclusive, which turns out suboptimal in our experiments. Thus, we reformulate both techniques based on binomial posteriors of multiple independent binary problems. This has two benefits; their performance is improved and they become more general, resulting in an add-on method that can boost virtually any WSSS method. This is demonstrated on a wide variety of baselines on the PASCAL VOC dataset, improving the region similarity and contour quality of all implemented state-of-the-art methods. Experiments on the MS COCO dataset show that our proposed add-on is well-suited for large-scale settings. Our code is available at https://github.com/arvijj/hfpl.  ( 3 min )

Decision trees are interpretable models that are well-suited to non-linear learning problems. Much work has been done on extending decision tree learning algorithms with differential privacy, a system that guarantees the privacy of samples within the training data. However, current state-of-the-art algorithms for this purpose sacrifice much utility for a small privacy benefit. These solutions create random decision nodes that reduce decision tree accuracy or spend an excessive share of the privacy budget on labeling leaves. Moreover, many works do not support continuous features or leak information about them. We propose a new method called PrivaTree based on private histograms that chooses good splits while consuming a small privacy budget. The resulting trees provide a significantly better privacy-utility trade-off and accept mixed numerical and categorical data without leaking information about numerical features. Finally, while it is notoriously hard to give robustness guarantees against data poisoning attacks, we demonstrate bounds for the expected accuracy and success rates of backdoor attacks against differentially-private learners. By leveraging the better privacy-utility trade-off of PrivaTree we are able to train decision trees with significantly better robustness against backdoor attacks compared to regular decision trees and with meaningful theoretical guarantees.  ( 2 min )

The distribution of the weights of modern deep neural networks (DNNs) - crucial for uncertainty quantification and robustness - is an eminently complex object due to its extremely high dimensionality. This paper proposes one of the first large-scale explorations of the posterior distribution of deep Bayesian Neural Networks (BNNs), expanding its study to real-world vision tasks and architectures. Specifically, we investigate the optimal approach for approximating the posterior, analyze the connection between posterior quality and uncertainty quantification, delve into the impact of modes on the posterior, and explore methods for visualizing the posterior. Moreover, we uncover weight-space symmetries as a critical aspect for understanding the posterior. To this extent, we develop an in-depth assessment of the impact of both permutation and scaling symmetries that tend to obfuscate the Bayesian posterior. While the first type of transformation is known for duplicating modes, we explore the relationship between the latter and L2 regularization, challenging previous misconceptions. Finally, to help the community improve our understanding of the Bayesian posterior, we will shortly release the first large-scale checkpoint dataset, including thousands of real-world models and our codes.

Despite the great progress of unsupervised domain adaptation (UDA) with the deep neural networks, current UDA models are opaque and cannot provide promising explanations, limiting their applications in the scenarios that require safe and controllable model decisions. At present, a surge of work focuses on designing deep interpretable methods with adequate data annotations and only a few methods consider the distributional shift problem. Most existing interpretable UDA methods are post-hoc ones, which cannot facilitate the model learning process for performance enhancement. In this paper, we propose an inherently interpretable method, named Transferable Conceptual Prototype Learning (TCPL), which could simultaneously interpret and improve the processes of knowledge transfer and decision-making in UDA. To achieve this goal, we design a hierarchically prototypical module that transfers categorical basic concepts from the source domain to the target domain and learns domain-shared prototypes for explaining the underlying reasoning process. With the learned transferable prototypes, a self-predictive consistent pseudo-label strategy that fuses confidence, predictions, and prototype information, is designed for selecting suitable target samples for pseudo annotations and gradually narrowing down the domain gap. Comprehensive experiments show that the proposed method can not only provide effective and intuitive explanations but also outperform previous state-of-the-arts.  ( 2 min )

Machine learning (ML) approaches are increasingly being used to accelerate combinatorial optimization (CO) problems. We look specifically at the Set Cover Problem (SCP) and propose Graph-SCP, a graph neural network method that can augment existing optimization solvers by learning to identify a much smaller sub-problem that contains the solution space. We evaluate the performance of Graph-SCP on synthetic weighted and unweighted SCP instances with diverse problem characteristics and complexities, and on instances from the OR Library, a canonical benchmark for SCP. We show that Graph-SCP reduces the problem size by 30-70% and achieves run time speedups up to~25x when compared to commercial solvers (Gurobi). Given a desired optimality threshold, Graph-SCP will improve upon it or even achieve 100% optimality. This is in contrast to fast greedy solutions that significantly compromise solution quality to achieve guaranteed polynomial run time. Graph-SCP can generalize to larger problem sizes and can be used with other conventional or ML-augmented CO solvers to lead to potential additional run time improvement.  ( 2 min )

Surrogate Optimization (SO) algorithms have shown promise for optimizing expensive black-box functions. However, their performance is heavily influenced by hyperparameters related to sampling and surrogate fitting, which poses a challenge to their widespread adoption. We investigate the impact of hyperparameters on various SO algorithms and propose a Hyperparameter Adaptive Search for SO (HASSO) approach. HASSO is not a hyperparameter tuning algorithm, but a generic self-adjusting SO algorithm that dynamically tunes its own hyperparameters while concurrently optimizing the primary objective function, without requiring additional evaluations. The aim is to improve the accessibility, effectiveness, and convergence speed of SO algorithms for practitioners. Our approach identifies and modifies the most influential hyperparameters specific to each problem and SO approach, reducing the need for manual tuning without significantly increasing the computational burden. Experimental results demonstrate the effectiveness of HASSO in enhancing the performance of various SO algorithms across different global optimization test problems.  ( 2 min )

Temporal difference learning (TD) is a foundational concept in reinforcement learning (RL), aimed at efficiently assessing a policy's value function. TD($\lambda$), a potent variant, incorporates a memory trace to distribute the prediction error into the historical context. However, this approach often neglects the significance of historical states and the relative importance of propagating the TD error, influenced by challenges such as visitation imbalance or outcome noise. To address this, we propose a novel TD algorithm named discerning TD learning (DTD), which allows flexible emphasis functions$-$predetermined or adapted during training$-$to allocate efforts effectively across states. We establish the convergence properties of our method within a specific class of emphasis functions and showcase its promising potential for adaptation to deep RL contexts. Empirical results underscore that employing a judicious emphasis function not only improves value estimation but also expedites learning across diverse scenarios.  ( 2 min )

In the realm of robotics, numerous downstream robotics tasks leverage machine learning methods for processing, modeling, or synthesizing data. Often, this data comprises variables that inherently carry geometric constraints, such as the unit-norm condition of quaternions representing rigid-body orientations or the positive definiteness of stiffness and manipulability ellipsoids. Handling such geometric constraints effectively requires the incorporation of tools from differential geometry into the formulation of machine learning methods. In this context, Riemannian manifolds emerge as a powerful mathematical framework to handle such geometric constraints. Nevertheless, their recent adoption in robot learning has been largely characterized by a mathematically-flawed simplification, hereinafter referred to as the ``single tangent space fallacy". This approach involves merely projecting the data of interest onto a single tangent (Euclidean) space, over which an off-the-shelf learning algorithm is applied. This paper provides a theoretical elucidation of various misconceptions surrounding this approach and offers experimental evidence of its shortcomings. Finally, it presents valuable insights to promote best practices when employing Riemannian geometry within robot learning applications.  ( 2 min )

Scaling of deep neural networks, especially Transformers, is pivotal for their surging performance and has further led to the emergence of sophisticated reasoning capabilities in foundation models. Such scaling generally requires training large models from scratch with random initialization, failing to leverage the knowledge acquired by their smaller counterparts, which are already resource-intensive to obtain. To tackle this inefficiency, we present $\textbf{L}$ossl$\textbf{E}$ss $\textbf{MO}$del Expansio$\textbf{N}$ (LEMON), a recipe to initialize scaled models using the weights of their smaller but pre-trained counterparts. This is followed by model training with an optimized learning rate scheduler tailored explicitly for the scaled models, substantially reducing the training time compared to training from scratch. Notably, LEMON is versatile, ensuring compatibility with various network structures, including models like Vision Transformers and BERT. Our empirical results demonstrate that LEMON reduces computational costs by 56.7% for Vision Transformers and 33.2% for BERT when compared to training from scratch.  ( 2 min )

It is well-established that large, diverse datasets play a pivotal role in the performance of modern AI systems for text and image modalities. However, there are no datasets for tabular data of comparable size and diversity to those available for text and images. Thus we present "TabLib'', a compilation of 627 million tables totaling 69 TiB, along with 867B tokens of context. TabLib was extracted from numerous file formats, including CSV, HTML, SQLite, PDF, Excel, and others, sourced from GitHub and Common Crawl. The size and diversity of TabLib offer considerable promise in the table modality, reminiscent of the original promise of foundational datasets for text and images, such as The Pile and LAION.  ( 2 min )

In this study, we present a graph neural network-based learning approach using an autoencoder setup to derive low-dimensional variables from features observed in experimental crystal structures. These variables are then biased in enhanced sampling to observe state-to-state transitions and reliable thermodynamic weights. Our approach uses simple convolution and pooling methods. To verify the effectiveness of our protocol, we examined the nucleation of various allotropes and polymorphs of iron and glycine from their molten states. Our graph latent variables when biased in well-tempered metadynamics consistently show transitions between states and achieve accurate free energy calculations in agreement with experiments, both of which are indicators of dependable sampling. This underscores the strength and promise of our graph neural net variables for improved sampling. The protocol shown here should be applicable for other systems and with other sampling methods.  ( 2 min )

We consider online reinforcement learning (RL) in episodic Markov decision processes (MDPs) under the linear $q^\pi$-realizability assumption, where it is assumed that the action-values of all policies can be expressed as linear functions of state-action features. This class is known to be more general than linear MDPs, where the transition kernel and the reward function are assumed to be linear functions of the feature vectors. As our first contribution, we show that the difference between the two classes is the presence of states in linearly $q^\pi$-realizable MDPs where for any policy, all the actions have approximately equal values, and skipping over these states by following an arbitrarily fixed policy in those states transforms the problem to a linear MDP. Based on this observation, we derive a novel (computationally inefficient) learning algorithm for linearly $q^\pi$-realizable MDPs that simultaneously learns what states should be skipped over and runs another learning algorithm on the linear MDP hidden in the problem. The method returns an $\epsilon$-optimal policy after $\text{polylog}(H, d)/\epsilon^2$ interactions with the MDP, where $H$ is the time horizon and $d$ is the dimension of the feature vectors, giving the first polynomial-sample-complexity online RL algorithm for this setting. The results are proved for the misspecified case, where the sample complexity is shown to degrade gracefully with the misspecification error.  ( 3 min )

By encoding time series as a string of numerical digits, we can frame time series forecasting as next-token prediction in text. Developing this approach, we find that large language models (LLMs) such as GPT-3 and LLaMA-2 can surprisingly zero-shot extrapolate time series at a level comparable to or exceeding the performance of purpose-built time series models trained on the downstream tasks. To facilitate this performance, we propose procedures for effectively tokenizing time series data and converting discrete distributions over tokens into highly flexible densities over continuous values. We argue the success of LLMs for time series stems from their ability to naturally represent multimodal distributions, in conjunction with biases for simplicity, and repetition, which align with the salient features in many time series, such as repeated seasonal trends. We also show how LLMs can naturally handle missing data without imputation through non-numerical text, accommodate textual side information, and answer questions to help explain predictions. While we find that increasing model size generally improves performance on time series, we show GPT-4 can perform worse than GPT-3 because of how it tokenizes numbers, and poor uncertainty calibration, which is likely the result of alignment interventions such as RLHF.  ( 2 min )

Station keeping is an essential maneuver for Autonomous Surface Vehicles (ASVs), mainly when used in confined spaces, to carry out surveys that require the ASV to keep its position or in collaboration with other vehicles where the relative position has an impact over the mission. However, this maneuver can become challenging for classic feedback controllers due to the need for an accurate model of the ASV dynamics and the environmental disturbances. This work proposes a Model Predictive Controller using Neural Network Simulation Error Minimization (NNSEM-MPC) to accurately predict the dynamics of the ASV under wind disturbances. The performance of the proposed scheme under wind disturbances is tested and compared against other controllers in simulation, using the Robotics Operating System (ROS) and the multipurpose simulation environment Gazebo. A set of six tests were conducted by combining two wind speeds (3 m/s and 6 m/s) and three wind directions (0$^\circ$, 90$^\circ$, and 180$^\circ$). The simulation results clearly show the advantage of the NNSEM-MPC over the following methods: backstepping controller, sliding mode controller, simplified dynamics MPC (SD-MPC), neural ordinary differential equation MPC (NODE-MPC), and knowledge-based NODE MPC (KNODE-MPC). The proposed NNSEM-MPC approach performs better than the rest in 4 out of the 6 test conditions, and it is the second best in the 2 remaining test cases, reducing the mean position and heading error by at least 31\% and 46\% respectively across all the test cases. In terms of execution speed, the proposed NNSEM-MPC is at least 36\% faster than the rest of the MPC controllers. The field experiments on two different ASV platforms showed that ASVs can effectively keep the station utilizing the proposed method, with a position error as low as $1.68$ m and a heading error as low as $6.14^{\circ}$ within time windows of at least $150$s.  ( 3 min )

Robotic learning for navigation in unfamiliar environments needs to provide policies for both task-oriented navigation (i.e., reaching a goal that the robot has located), and task-agnostic exploration (i.e., searching for a goal in a novel setting). Typically, these roles are handled by separate models, for example by using subgoal proposals, planning, or separate navigation strategies. In this paper, we describe how we can train a single unified diffusion policy to handle both goal-directed navigation and goal-agnostic exploration, with the latter providing the ability to search novel environments, and the former providing the ability to reach a user-specified goal once it has been located. We show that this unified policy results in better overall performance when navigating to visually indicated goals in novel environments, as compared to approaches that use subgoal proposals from generative models, or prior methods based on latent variable models. We instantiate our method by using a large-scale Transformer-based policy trained on data from multiple ground robots, with a diffusion model decoder to flexibly handle both goal-conditioned and goal-agnostic navigation. Our experiments, conducted on a real-world mobile robot platform, show effective navigation in unseen environments in comparison with five alternative methods, and demonstrate significant improvements in performance and lower collision rates, despite utilizing smaller models than state-of-the-art approaches. For more videos, code, and pre-trained model checkpoints, see https://general-navigation-models.github.io/nomad/  ( 2 min )

Distributed optimization methods with random communication skips are gaining increasing attention due to their proven benefits in accelerating communication complexity. Nevertheless, existing research mainly focuses on centralized communication protocols for strongly convex deterministic settings. In this work, we provide a decentralized optimization method called RandCom, which incorporates probabilistic local updates. We analyze the performance of RandCom in stochastic non-convex, convex, and strongly convex settings and demonstrate its ability to asymptotically reduce communication overhead by the probability of communication. Additionally, we prove that RandCom achieves linear speedup as the number of nodes increases. In stochastic strongly convex settings, we further prove that RandCom can achieve linear speedup with network-independent stepsizes. Moreover, we apply RandCom to federated learning and provide positive results concerning the potential for achieving linear speedup and the suitability of the probabilistic local update approach for non-convex settings.  ( 2 min )

For over two decades, detecting rare events has been a challenging task among researchers in the data mining and machine learning domain. Real-life problems inspire researchers to navigate and further improve data processing and algorithmic approaches to achieve effective and computationally efficient methods for imbalanced learning. In this paper, we have collected and reviewed 258 peer-reviewed papers from archival journals and conference papers in an attempt to provide an in-depth review of various approaches in imbalanced learning from technical and application perspectives. This work aims to provide a structured review of methods used to address the problem of imbalanced data in various domains and create a general guideline for researchers in academia or industry who want to dive into the broad field of machine learning using large-scale imbalanced data.  ( 2 min )

The current era of quantum computing has yielded several algorithms that promise high computational efficiency. While the algorithms are sound in theory and can provide potentially exponential speedup, there is little guidance on how to design proper quantum circuits to realize the appropriate unitary transformation to be applied to the input quantum state. In this paper, we present \texttt{QArchSearch}, an AI based quantum architecture search package with the \texttt{QTensor} library as a backend that provides a principled and automated approach to finding the best model given a task and input quantum state. We show that the search package is able to efficiently scale the search to large quantum circuits and enables the exploration of more complex models for different quantum applications. \texttt{QArchSearch} runs at scale and high efficiency on high-performance computing systems using a two-level parallelization scheme on both CPUs and GPUs, which has been demonstrated on the Polaris supercomputer.  ( 2 min )

Learning rate schedules used in practice bear little resemblance to those recommended by theory. We close much of this theory/practice gap, and as a consequence are able to derive new problem-adaptive learning rate schedules. Our key technical contribution is a refined analysis of learning rate schedules for a wide class of optimization algorithms (including SGD). In contrast to most prior works that study the convergence of the average iterate, we study the last iterate, which is what most people use in practice. When considering only worst-case analysis, our theory predicts that the best choice is the linear decay schedule: a popular choice in practice that sets the stepsize proportionally to $1 - t/T$, where $t$ is the current iteration and $T$ is the total number of steps. To go beyond this worst-case analysis, we use the observed gradient norms to derive schedules refined for any particular task. These refined schedules exhibit learning rate warm-up and rapid learning rate annealing near the end of training. Ours is the first systematic approach to automatically yield both of these properties. We perform the most comprehensive evaluation of learning rate schedules to date, evaluating across 10 diverse deep learning problems, a series of LLMs, and a suite of logistic regression problems. We validate that overall, the linear-decay schedule matches or outperforms all commonly used default schedules including cosine annealing, and that our schedule refinement method gives further improvements.  ( 3 min )

Fully-connected deep neural networks with weights initialized from independent Gaussian distributions can be tuned to criticality, which prevents the exponential growth or decay of signals propagating through the network. However, such networks still exhibit fluctuations that grow linearly with the depth of the network, which may impair the training of networks with width comparable to depth. We show analytically that rectangular networks with tanh activations and weights initialized from the ensemble of orthogonal matrices have corresponding preactivation fluctuations which are independent of depth, to leading order in inverse width. Moreover, we demonstrate numerically that, at initialization, all correlators involving the neural tangent kernel (NTK) and its descendants at leading order in inverse width -- which govern the evolution of observables during training -- saturate at a depth of $\sim 20$, rather than growing without bound as in the case of Gaussian initializations. We speculate that this structure preserves finite-width feature learning while reducing overall noise, thus improving both generalization and training speed. We provide some experimental justification by relating empirical measurements of the NTK to the superior performance of deep nonlinear orthogonal networks trained under full-batch gradient descent on the MNIST and CIFAR-10 classification tasks.  ( 2 min )

A common approach to explain NLP models, is to use importance measures that express which tokens are important for a prediction. Unfortunately, such explanations are often wrong despite being persuasive. Therefore, it is essential to measure their faithfulness. One such metric is if tokens are truly important, then masking them should result in worse model performance. However, token masking introduces out-of-distribution issues and existing solutions are computationally expensive and employ proxy-models. Furthermore, other metrics are very limited in scope. In this work, we propose an inherently faithfulness measurable model that addresses these challenges. This is achieved by using a novel fine-tuning method that incorporates masking, such that masking tokens become in-distribution by design. This differs from existing approaches, which are completely model-agnostic but are inapplicable in practice. We demonstrate the generality of our approach by applying it to various tasks and validate it using statistical in-distribution tests. Additionally, because masking is in-distribution, importance measures which themselves use masking become more faithful, thus our model becomes more explainable.  ( 2 min )

This paper discusses predictive performance and processes undertaken on flight pricing data utilizing r2(r-square) and RMSE that leverages a large dataset, originally from Expedia.com, consisting of approximately 20 million records or 4.68 gigabytes. The project aims to determine the best models usable in the real world to predict airline ticket fares for non-stop flights across the US. Therefore, good generalization capability and optimized processing times are important measures for the model. We will discover key business insights utilizing feature importance and discuss the process and tools used for our analysis. Four regression machine learning algorithms were utilized: Random Forest, Gradient Boost Tree, Decision Tree, and Factorization Machines utilizing Cross Validator and Training Validator functions for assessing performance and generalization capability.  ( 2 min )

Self-supervised representation learning~(SSRL) has advanced considerably by exploiting the transformation invariance assumption under artificially designed data augmentations. While augmentation-based SSRL algorithms push the boundaries of performance in computer vision and natural language processing, they are often not directly applicable to other data modalities, and can conflict with application-specific data augmentation constraints. This paper presents an SSRL approach that can be applied to any data modality and network architecture because it does not rely on augmentations or masking. Specifically, we show that high-quality data representations can be learned by reconstructing random data projections. We evaluate the proposed approach on a wide range of representation learning tasks that span diverse modalities and real-world applications. We show that it outperforms multiple state-of-the-art SSRL baselines. Due to its wide applicability and strong empirical results, we argue that learning from randomness is a fruitful research direction worthy of attention and further study.  ( 2 min )

Activation functions (AFs) are crucial components of deep neural networks (DNNs), having a significant impact on their performance. An activation function in a DNN is typically a smooth, nonlinear function that transforms an input signal into an output signal for the subsequent layer. In this paper, we propose the Parametric Leaky Tanh (PLTanh), a novel hybrid activation function designed to combine the strengths of both the Tanh and Leaky ReLU (LReLU) activation functions. PLTanh is differentiable at all points and addresses the 'dying ReLU' problem by ensuring a non-zero gradient for negative inputs, consistent with the behavior of LReLU. By integrating the unique advantages of these two diverse activation functions, PLTanh facilitates the learning of more intricate nonlinear relationships within the network. This paper presents an empirical evaluation of PLTanh against established activation functions, namely ReLU, LReLU, and ALReLU utilizing five diverse datasets.  ( 2 min )

The challenge of navigation in environments with dynamic objects continues to be a central issue in the study of autonomous agents. While predictive methods hold promise, their reliance on precise state information makes them less practical for real-world implementation. This study presents visual forecasting as an innovative alternative. By introducing intuitive visual cues, this approach projects the future trajectories of dynamic objects to improve agent perception and enable anticipatory actions. Our research explores two distinct strategies for conveying predictive information through visual forecasting: (1) sequences of bounding boxes, and (2) augmented paths. To validate the proposed visual forecasting strategies, we initiate evaluations in simulated environments using the Unity engine and then extend these evaluations to real-world scenarios to assess both practicality and effectiveness. The results confirm the viability of visual forecasting as a promising solution for navigation and obstacle avoidance in dynamic environments.  ( 2 min )

Fully-connected deep neural networks with weights initialized from independent Gaussian distributions can be tuned to criticality, which prevents the exponential growth or decay of signals propagating through the network. However, such networks still exhibit fluctuations that grow linearly with the depth of the network, which may impair the training of networks with width comparable to depth. We show analytically that rectangular networks with tanh activations and weights initialized from the ensemble of orthogonal matrices have corresponding preactivation fluctuations which are independent of depth, to leading order in inverse width. Moreover, we demonstrate numerically that, at initialization, all correlators involving the neural tangent kernel (NTK) and its descendants at leading order in inverse width -- which govern the evolution of observables during training -- saturate at a depth of $\sim 20$, rather than growing without bound as in the case of Gaussian initializations. We speculate that this structure preserves finite-width feature learning while reducing overall noise, thus improving both generalization and training speed. We provide some experimental justification by relating empirical measurements of the NTK to the superior performance of deep nonlinear orthogonal networks trained under full-batch gradient descent on the MNIST and CIFAR-10 classification tasks.  ( 2 min )

Scaling of deep neural networks, especially Transformers, is pivotal for their surging performance and has further led to the emergence of sophisticated reasoning capabilities in foundation models. Such scaling generally requires training large models from scratch with random initialization, failing to leverage the knowledge acquired by their smaller counterparts, which are already resource-intensive to obtain. To tackle this inefficiency, we present $\textbf{L}$ossl$\textbf{E}$ss $\textbf{MO}$del Expansio$\textbf{N}$ (LEMON), a recipe to initialize scaled models using the weights of their smaller but pre-trained counterparts. This is followed by model training with an optimized learning rate scheduler tailored explicitly for the scaled models, substantially reducing the training time compared to training from scratch. Notably, LEMON is versatile, ensuring compatibility with various network structures, including models like Vision Transformers and BERT. Our empirical results demonstrate that LEMON reduces computational costs by 56.7% for Vision Transformers and 33.2% for BERT when compared to training from scratch.

Consider the problem of estimating a random variable $X$ from noisy observations $Y = X+ Z$, where $Z$ is standard normal, under the $L^1$ fidelity criterion. It is well known that the optimal Bayesian estimator in this setting is the conditional median. This work shows that the only prior distribution on $X$ that induces linearity in the conditional median is Gaussian. Along the way, several other results are presented. In particular, it is demonstrated that if the conditional distribution $P_{X|Y=y}$ is symmetric for all $y$, then $X$ must follow a Gaussian distribution. Additionally, we consider other $L^p$ losses and observe the following phenomenon: for $p \in [1,2]$, Gaussian is the only prior distribution that induces a linear optimal Bayesian estimator, and for $p \in (2,\infty)$, infinitely many prior distributions on $X$ can induce linearity. Finally, extensions are provided to encompass noise models leading to conditional distributions from certain exponential families.

The distribution of the weights of modern deep neural networks (DNNs) - crucial for uncertainty quantification and robustness - is an eminently complex object due to its extremely high dimensionality. This paper proposes one of the first large-scale explorations of the posterior distribution of deep Bayesian Neural Networks (BNNs), expanding its study to real-world vision tasks and architectures. Specifically, we investigate the optimal approach for approximating the posterior, analyze the connection between posterior quality and uncertainty quantification, delve into the impact of modes on the posterior, and explore methods for visualizing the posterior. Moreover, we uncover weight-space symmetries as a critical aspect for understanding the posterior. To this extent, we develop an in-depth assessment of the impact of both permutation and scaling symmetries that tend to obfuscate the Bayesian posterior. While the first type of transformation is known for duplicating modes, we explore the relationship between the latter and L2 regularization, challenging previous misconceptions. Finally, to help the community improve our understanding of the Bayesian posterior, we will shortly release the first large-scale checkpoint dataset, including thousands of real-world models and our codes.

This paper introduces a novel model-agnostic algorithm called adaptive ensemble batch multi-input multi-output conformalized quantile regression (AEnbMIMOCQR} that enables forecasters to generate multi-step ahead prediction intervals for a fixed pre-specified miscoverage rate in a distribution-free manner. Our method is grounded on conformal prediction principles, however, it does not require data splitting and provides close to exact coverage even when the data is not exchangeable. Moreover, the resulting prediction intervals, besides being empirically valid along the forecast horizon, do not neglect heteroscedasticity. AEnbMIMOCQR is designed to be robust to distribution shifts, which means that its prediction intervals remain reliable over an unlimited period of time, without entailing retraining or imposing unrealistic strict assumptions on the data-generating process. Through methodically experimentation, we demonstrate that our approach outperforms other competitive methods on both real-world and synthetic datasets. The code used in the experimental part and a tutorial on how to use AEnbMIMOCQR can be found at the following GitHub repository: https://github.com/Quilograma/AEnbMIMOCQR.

Distributed optimization methods with random communication skips are gaining increasing attention due to their proven benefits in accelerating communication complexity. Nevertheless, existing research mainly focuses on centralized communication protocols for strongly convex deterministic settings. In this work, we provide a decentralized optimization method called RandCom, which incorporates probabilistic local updates. We analyze the performance of RandCom in stochastic non-convex, convex, and strongly convex settings and demonstrate its ability to asymptotically reduce communication overhead by the probability of communication. Additionally, we prove that RandCom achieves linear speedup as the number of nodes increases. In stochastic strongly convex settings, we further prove that RandCom can achieve linear speedup with network-independent stepsizes. Moreover, we apply RandCom to federated learning and provide positive results concerning the potential for achieving linear speedup and the suitability of the probabilistic local update approach for non-convex settings.  ( 2 min )

The 2022 National Defense Strategy of the United States listed climate change as a serious threat to national security. Climate intervention methods, such as stratospheric aerosol injection, have been proposed as mitigation strategies, but the downstream effects of such actions on a complex climate system are not well understood. The development of algorithmic techniques for quantifying relationships between source and impact variables related to a climate event (i.e., a climate pathway) would help inform policy decisions. Data-driven deep learning models have become powerful tools for modeling highly nonlinear relationships and may provide a route to characterize climate variable relationships. In this paper, we explore the use of an echo state network (ESN) for characterizing climate pathways. ESNs are a computationally efficient neural network variation designed for temporal data, and recent work proposes ESNs as a useful tool for forecasting spatio-temporal climate data. Like other neural networks, ESNs are non-interpretable black-box models, which poses a hurdle for understanding variable relationships. We address this issue by developing feature importance methods for ESNs in the context of spatio-temporal data to quantify variable relationships captured by the model. We conduct a simulation study to assess and compare the feature importance techniques, and we demonstrate the approach on reanalysis climate data. In the climate application, we select a time period that includes the 1991 volcanic eruption of Mount Pinatubo. This event was a significant stratospheric aerosol injection, which we use as a proxy for an artificial stratospheric aerosol injection. Using the proposed approach, we are able to characterize relationships between pathway variables associated with this event.  ( 3 min )

Incomplete knowledge of the environment leads an agent to make decisions under uncertainty. One of the major dilemmas in Reinforcement Learning (RL) where an autonomous agent has to balance two contrasting needs in making its decisions is: exploiting the current knowledge of the environment to maximize the cumulative reward as well as exploring actions that allow improving the knowledge of the environment, hopefully leading to higher reward values (exploration-exploitation trade-off). Concurrently, another relevant issue regards the full observability of the states, which may not be assumed in all applications. Such as when only 2D images are considered as input in a RL approach used for finding the optimal action within a 3D simulation environment. In this work, we address these issues by deploying and testing several techniques to balance exploration and exploitation trade-off on partially observable systems for predicting steering wheels in autonomous driving scenario. More precisely, the final aim is to investigate the effects of using both stochastic and deterministic multi-armed bandit strategies coupled with a Deep Recurrent Q-Network. Additionally, we adapted and evaluated the impact of an innovative method to improve the learning phase of the underlying Convolutional Recurrent Neural Network. We aim to show that adaptive stochastic methods for exploration better approximate the trade-off between exploration and exploitation as, in general, Softmax and Max-Boltzmann strategies are able to outperform epsilon-greedy techniques.  ( 2 min )

Matrix-variate distributions are a recent addition to the model-based clustering field, thereby making it possible to analyze data in matrix form with complex structure such as images and time series. Due to its recent appearance, there is limited literature on matrix-variate data, with even less on dealing with outliers in these models. An approach for clustering matrix-variate normal data with outliers is discussed. The approach, which uses the distribution of subset log-likelihoods, extends the OCLUST algorithm to matrix-variate normal data and uses an iterative approach to detect and trim outliers.  ( 2 min )

Interpretable policy learning seeks to estimate intelligible decision policies from observed actions; however, existing models fall short by forcing a tradeoff between accuracy and interpretability. This tradeoff limits data-driven interpretations of human decision-making process. e.g. to audit medical decisions for biases and suboptimal practices, we require models of decision processes which provide concise descriptions of complex behaviors. Fundamentally, existing approaches are burdened by this tradeoff because they represent the underlying decision process as a universal policy, when in fact human decisions are dynamic and can change drastically with contextual information. Thus, we propose Contextualized Policy Recovery (CPR), which re-frames the problem of modeling complex decision processes as a multi-task learning problem in which complex decision policies are comprised of context-specific policies. CPR models each context-specific policy as a linear observation-to-action mapping, and generates new decision models $\textit{on-demand}$ as contexts are updated with new observations. CPR is compatible with fully offline and partially observable decision environments, and can be tailored to incorporate any recurrent black-box model or interpretable decision model. We assess CPR through studies on simulated and real data, achieving state-of-the-art performance on the canonical tasks of predicting antibiotic prescription in intensive care units ($+22\%$ AUROC vs. previous SOTA) and predicting MRI prescription for Alzheimer's patients ($+7.7\%$ AUROC vs. previous SOTA). With this improvement in predictive performance, CPR closes the accuracy gap between interpretable and black-box methods for policy learning, allowing high-resolution exploration and analysis of context-specific decision models.  ( 3 min )

Larger and deeper networks generalise well despite their increased capacity to overfit. Understanding why this happens is theoretically and practically important. One recent approach looks at the infinitely wide limits of such networks and their corresponding kernels. However, these theoretical tools cannot fully explain finite networks as the empirical kernel changes significantly during gradient-descent-based training in contrast to infinite networks. In this work, we derive an iterative linearised training method as a novel empirical tool to further investigate this distinction, allowing us to control for sparse (i.e. infrequent) feature updates and quantify the frequency of feature learning needed to achieve comparable performance. We justify iterative linearisation as an interpolation between a finite analog of the infinite width regime, which does not learn features, and standard gradient descent training, which does. Informally, we also show that it is analogous to a damped version of the Gauss-Newton algorithm -- a second-order method. We show that in a variety of cases, iterative linearised training surprisingly performs on par with standard training, noting in particular how much less frequent feature learning is required to achieve comparable performance. We also show that feature learning is essential for good performance. Since such feature learning inevitably causes changes in the NTK kernel, we provide direct negative evidence for the NTK theory, which states the NTK kernel remains constant during training.  ( 3 min )

Theoretical studies on transfer learning or domain adaptation have so far focused on situations with a known hypothesis class or model; however in practice, some amount of model selection is usually involved, often appearing under the umbrella term of hyperparameter-tuning: for example, one may think of the problem of tuning for the right neural network architecture towards a target task, while leveraging data from a related source task. Now, in addition to the usual tradeoffs on approximation vs estimation errors involved in model selection, this problem brings in a new complexity term, namely, the transfer distance between source and target distributions, which is known to vary with the choice of hypothesis class. We present a first study of this problem, focusing on classification; in particular, the analysis reveals some remarkable phenomena: adaptive rates, i.e., those achievable with no distributional information, can be arbitrarily slower than oracle rates, i.e., when given knowledge on distances.  ( 2 min )

Feature learning is thought to be one of the fundamental reasons for the success of deep neural networks. It is rigorously known that in two-layer fully-connected neural networks under certain conditions, one step of gradient descent on the first layer followed by ridge regression on the second layer can lead to feature learning; characterized by the appearance of a separated rank-one component -- spike -- in the spectrum of the feature matrix. However, with a constant gradient descent step size, this spike only carries information from the linear component of the target function and therefore learning non-linear components is impossible. We show that with a learning rate that grows with the sample size, such training in fact introduces multiple rank-one components, each corresponding to a specific polynomial feature. We further prove that the limiting large-dimensional and large sample training and test errors of the updated neural networks are fully characterized by these spikes. By precisely analyzing the improvement in the loss, we demonstrate that these non-linear features can enhance learning.  ( 2 min )

We propose an approach utilizing gamma-distributed random variables, coupled with log-Gaussian modeling, to generate synthetic datasets suitable for training neural networks. This addresses the challenge of limited real observations in various applications. We apply this methodology to both Raman and coherent anti-Stokes Raman scattering (CARS) spectra, using experimental spectra to estimate gamma process parameters. Parameter estimation is performed using Markov chain Monte Carlo methods, yielding a full Bayesian posterior distribution for the model which can be sampled for synthetic data generation. Additionally, we model the additive and multiplicative background functions for Raman and CARS with Gaussian processes. We train two Bayesian neural networks to estimate parameters of the gamma process which can then be used to estimate the underlying Raman spectrum and simultaneously provide uncertainty through the estimation of parameters of a probability distribution. We apply the trained Bayesian neural networks to experimental Raman spectra of phthalocyanine blue, aniline black, naphthol red, and red 264 pigments and also to experimental CARS spectra of adenosine phosphate, fructose, glucose, and sucrose. The results agree with deterministic point estimates for the underlying Raman and CARS spectral signatures.  ( 2 min )

In this work, we present an approach to construct a video-based robot policy capable of reliably executing diverse tasks across different robots and environments from few video demonstrations without using any action annotations. Our method leverages images as a task-agnostic representation, encoding both the state and action information, and text as a general representation for specifying robot goals. By synthesizing videos that ``hallucinate'' robot executing actions and in combination with dense correspondences between frames, our approach can infer the closed-formed action to execute to an environment without the need of any explicit action labels. This unique capability allows us to train the policy solely based on RGB videos and deploy learned policies to various robotic tasks. We demonstrate the efficacy of our approach in learning policies on table-top manipulation and navigation tasks. Additionally, we contribute an open-source framework for efficient video modeling, enabling the training of high-fidelity policy models with four GPUs within a single day.  ( 2 min )

Score-based Generative Models (SGMs) have demonstrated exceptional synthesis outcomes across various tasks. However, the current design landscape of the forward diffusion process remains largely untapped and often relies on physical heuristics or simplifying assumptions. Utilizing insights from the development of scalable Bayesian posterior samplers, we present a complete recipe for formulating forward processes in SGMs, ensuring convergence to the desired target distribution. Our approach reveals that several existing SGMs can be seen as specific manifestations of our framework. Building upon this method, we introduce Phase Space Langevin Diffusion (PSLD), which relies on score-based modeling within an augmented space enriched by auxiliary variables akin to physical phase space. Empirical results exhibit the superior sample quality and improved speed-quality trade-off of PSLD compared to various competing approaches on established image synthesis benchmarks. Remarkably, PSLD achieves sample quality akin to state-of-the-art SGMs (FID: 2.10 for unconditional CIFAR-10 generation). Lastly, we demonstrate the applicability of PSLD in conditional synthesis using pre-trained score networks, offering an appealing alternative as an SGM backbone for future advancements. Code and model checkpoints can be accessed at \url{https://github.com/mandt-lab/PSLD}.  ( 2 min )

In data-driven optimization, sample average approximation (SAA) is known to suffer from the so-called optimizer's curse that causes an over-optimistic evaluation of the solution performance. We argue that a special type of distributionallly robust optimization (DRO) formulation offers theoretical advantages in correcting for this optimizer's curse compared to simple ``margin'' adjustments to SAA and other DRO approaches: It attains a statistical bound on the out-of-sample performance, for a wide class of objective functions and distributions, that is nearly tightest in terms of exponential decay rate. This DRO uses an ambiguity set based on a Kullback Leibler (KL) divergence smoothed by the Wasserstein or L\'evy-Prokhorov (LP) distance via a suitable distance optimization. Computationally, we also show that such a DRO, and its generalized versions using smoothed $f$-divergence, are not harder than DRO problems based on $f$-divergence or Wasserstein distances, rendering our DRO formulations both statistically optimal and computationally viable.  ( 2 min )

Sparse logistic regression is for classification and feature selection simultaneously. Although many studies have been done to solve $\ell_1$-regularized logistic regression, there is no equivalently abundant work on solving sparse logistic regression with nonconvex regularization term. In this paper, we propose a unified framework to solve $\ell_1$-regularized logistic regression, which can be naturally extended to nonconvex regularization term, as long as certain requirement is satisfied. In addition, we also utilize a different line search criteria to guarantee monotone convergence for various regularization terms. Empirical experiments on binary classification tasks with real-world datasets demonstrate our proposed algorithms are capable of performing classification and feature selection effectively at a lower computational cost.  ( 2 min )

Learning rate schedules used in practice bear little resemblance to those recommended by theory. We close much of this theory/practice gap, and as a consequence are able to derive new problem-adaptive learning rate schedules. Our key technical contribution is a refined analysis of learning rate schedules for a wide class of optimization algorithms (including SGD). In contrast to most prior works that study the convergence of the average iterate, we study the last iterate, which is what most people use in practice. When considering only worst-case analysis, our theory predicts that the best choice is the linear decay schedule: a popular choice in practice that sets the stepsize proportionally to $1 - t/T$, where $t$ is the current iteration and $T$ is the total number of steps. To go beyond this worst-case analysis, we use the observed gradient norms to derive schedules refined for any particular task. These refined schedules exhibit learning rate warm-up and rapid learning rate annealing near the end of training. Ours is the first systematic approach to automatically yield both of these properties. We perform the most comprehensive evaluation of learning rate schedules to date, evaluating across 10 diverse deep learning problems, a series of LLMs, and a suite of logistic regression problems. We validate that overall, the linear-decay schedule matches or outperforms all commonly used default schedules including cosine annealing, and that our schedule refinement method gives further improvements.  ( 3 min )

This paper focuses on the problem of Differentially Private Stochastic Optimization for (multi-layer) fully connected neural networks with a single output node. In the first part, we examine cases with no hidden nodes, specifically focusing on Generalized Linear Models (GLMs). We investigate the well-specific model where the random noise possesses a zero mean, and the link function is both bounded and Lipschitz continuous. We propose several algorithms and our analysis demonstrates the feasibility of achieving an excess population risk that remains invariant to the data dimension. We also delve into the scenario involving the ReLU link function, and our findings mirror those of the bounded link function. We conclude this section by contrasting well-specified and misspecified models, using ReLU regression as a representative example. In the second part of the paper, we extend our ideas to two-layer neural networks with sigmoid or ReLU activation functions in the well-specified model. In the third part, we study the theoretical guarantees of DP-SGD in Abadi et al. (2016) for fully connected multi-layer neural networks. By utilizing recent advances in Neural Tangent Kernel theory, we provide the first excess population risk when both the sample size and the width of the network are sufficiently large. Additionally, we discuss the role of some parameters in DP-SGD regarding their utility, both theoretically and empirically.  ( 2 min )

Generative adversarial networks (GANs) have been extremely successful in generating samples, from seemingly high dimensional probability measures. However, these methods struggle to capture the temporal dependence of joint probability distributions induced by time-series data. Furthermore, long time-series data streams hugely increase the dimension of the target space, which may render generative modelling infeasible. To overcome these challenges, motivated by the autoregressive models in econometric, we are interested in the conditional distribution of future time series given the past information. We propose the generic conditional Sig-WGAN framework by integrating Wasserstein-GANs (WGANs) with mathematically principled and efficient path feature extraction called the signature of a path. The signature of a path is a graded sequence of statistics that provides a universal description for a stream of data, and its expected value characterises the law of the time-series model. In particular, we develop the conditional Sig-$W_1$ metric, that captures the conditional joint law of time series models, and use it as a discriminator. The signature feature space enables the explicit representation of the proposed discriminators which alleviates the need for expensive training. We validate our method on both synthetic and empirical dataset and observe that our method consistently and significantly outperforms state-of-the-art benchmarks with respect to measures of similarity and predictive ability.  ( 3 min )

This study proposes an interpretable neural network-based non-proportional odds model (N$^3$POM) for ordinal regression. N$^3$POM is different from conventional approaches to ordinal regression with non-proportional models in several ways: (1) N$^3$POM is designed to directly handle continuous responses, whereas standard methods typically treat de facto ordered continuous variables as discrete, (2) instead of estimating response-dependent finite coefficients of linear models from discrete responses as is done in conventional approaches, we train a non-linear neural network to serve as a coefficient function. Thanks to the neural network, N$^3$POM offers flexibility while preserving the interpretability of conventional ordinal regression. We establish a sufficient condition under which the predicted conditional cumulative probability locally satisfies the monotonicity constraint over a user-specified region in the covariate space. Additionally, we provide a monotonicity-preserving stochastic (MPS) algorithm for effectively training the neural network. We apply N$^3$POM to several real-world datasets.  ( 2 min )

Neural ordinary differential equations (neural ODEs) are a popular family of continuous-depth deep learning models. In this work, we consider a large family of parameterized ODEs with continuous-in-time parameters, which include time-dependent neural ODEs. We derive a generalization bound for this class by a Lipschitz-based argument. By leveraging the analogy between neural ODEs and deep residual networks, our approach yields in particular a generalization bound for a class of deep residual networks. The bound involves the magnitude of the difference between successive weight matrices. We illustrate numerically how this quantity affects the generalization capability of neural networks.  ( 2 min )

Detecting out-of-distribution (OOD) data is a critical challenge in machine learning due to model overconfidence, often without awareness of their epistemological limits. We hypothesize that ``neural collapse'', a phenomenon affecting in-distribution data for models trained beyond loss convergence, also influences OOD data. To benefit from this interplay, we introduce NECO, a novel post-hoc method for OOD detection, which leverages the geometric properties of ``neural collapse'' and of principal component spaces to identify OOD data. Our extensive experiments demonstrate that NECO achieves state-of-the-art results on both small and large-scale OOD detection tasks while exhibiting strong generalization capabilities across different network architectures. Furthermore, we provide a theoretical explanation for the effectiveness of our method in OOD detection. We plan to release the code after the anonymity period.  ( 2 min )

Existing regression models tend to fall short in both accuracy and uncertainty estimation when the label distribution is imbalanced. In this paper, we propose a probabilistic deep learning model, dubbed variational imbalanced regression (VIR), which not only performs well in imbalanced regression but naturally produces reasonable uncertainty estimation as a byproduct. Different from typical variational autoencoders assuming I.I.D. representations (a data point's representation is not directly affected by other data points), our VIR borrows data with similar regression labels to compute the latent representation's variational distribution; furthermore, different from deterministic regression models producing point estimates, VIR predicts the entire normal-inverse-gamma distributions and modulates the associated conjugate distributions to impose probabilistic reweighting on the imbalanced data, thereby providing better uncertainty estimation. Experiments in several real-world datasets show that our VIR can outperform state-of-the-art imbalanced regression models in terms of both accuracy and uncertainty estimation. Code will soon be available at \url{https://github.com/Wang-ML-Lab/variational-imbalanced-regression}.  ( 2 min )

Surrogate Optimization (SO) algorithms have shown promise for optimizing expensive black-box functions. However, their performance is heavily influenced by hyperparameters related to sampling and surrogate fitting, which poses a challenge to their widespread adoption. We investigate the impact of hyperparameters on various SO algorithms and propose a Hyperparameter Adaptive Search for SO (HASSO) approach. HASSO is not a hyperparameter tuning algorithm, but a generic self-adjusting SO algorithm that dynamically tunes its own hyperparameters while concurrently optimizing the primary objective function, without requiring additional evaluations. The aim is to improve the accessibility, effectiveness, and convergence speed of SO algorithms for practitioners. Our approach identifies and modifies the most influential hyperparameters specific to each problem and SO approach, reducing the need for manual tuning without significantly increasing the computational burden. Experimental results demonstrate the effectiveness of HASSO in enhancing the performance of various SO algorithms across different global optimization test problems.  ( 2 min )

In 1-bit compressed sensing, the aim is to estimate a $k$-sparse unit vector $x\in S^{n-1}$ within an $\epsilon$ error (in $\ell_2$) from minimal number of linear measurements that are quantized to just their signs, i.e., from measurements of the form $y = \mathrm{Sign}(\langle a, x\rangle).$ In this paper, we study a noisy version where a fraction of the measurements can be flipped, potentially by an adversary. In particular, we analyze the Binary Iterative Hard Thresholding (BIHT) algorithm, a proximal gradient descent on a properly defined loss function used for 1-bit compressed sensing, in this noisy setting. It is known from recent results that, with $\tilde{O}(\frac{k}{\epsilon})$ noiseless measurements, BIHT provides an estimate within $\epsilon$ error. This result is optimal and universal, meaning one set of measurements work for all sparse vectors. In this paper, we show that BIHT also provides better results than all known methods for the noisy setting. We show that when up to $\tau$-fraction of the sign measurements are incorrect (adversarial error), with the same number of measurements as before, BIHT agnostically provides an estimate of $x$ within an $\tilde{O}(\epsilon+\tau)$ error, maintaining the universality of measurements. This establishes stability of iterative hard thresholding in the presence of measurement error. To obtain the result, we use the restricted approximate invertibility of Gaussian matrices, as well as a tight analysis of the high-dimensional geometry of the adversarially corrupted measurements.  ( 3 min )

Hierarchical time series are common in several applied fields. The forecasts for these time series are required to be coherent, that is, to satisfy the constraints given by the hierarchy. The most popular technique to enforce coherence is called reconciliation, which adjusts the base forecasts computed for each time series. However, recent works on probabilistic reconciliation present several limitations. In this paper, we propose a new approach based on conditioning to reconcile any type of forecast distribution. We then introduce a new algorithm, called Bottom-Up Importance Sampling, to efficiently sample from the reconciled distribution. It can be used for any base forecast distribution: discrete, continuous, or in the form of samples, providing a major speedup compared to the current methods. Experiments on several temporal hierarchies show a significant improvement over base probabilistic forecasts.  ( 2 min )

In practice, deep neural networks are often able to easily interpolate their training data. To understand this phenomenon, many works have aimed to quantify the memorization capacity of a neural network architecture: the largest number of points such that the architecture can interpolate any placement of these points with any assignment of labels. For real-world data, however, one intuitively expects the presence of a benign structure so that interpolation already occurs at a smaller network size than suggested by memorization capacity. In this paper, we investigate interpolation by adopting an instance-specific viewpoint. We introduce a simple randomized algorithm that, given a fixed finite dataset with two classes, with high probability constructs an interpolating three-layer neural network in polynomial time. The required number of parameters is linked to geometric properties of the two classes and their mutual arrangement. As a result, we obtain guarantees that are independent of the number of samples and hence move beyond worst-case memorization capacity bounds. We illustrate the effectiveness of the algorithm in non-pathological situations with extensive numerical experiments and link the insights back to the theoretical results.  ( 2 min )

Many causal estimands are only partially identifiable since they depend on the unobservable joint distribution between potential outcomes. Stratification on pretreatment covariates can yield sharper partial identification bounds; however, unless the covariates are discrete with relatively small support, this approach typically requires consistent estimation of the conditional distributions of the potential outcomes given the covariates. Thus, existing approaches may fail under model misspecification or if consistency assumptions are violated. In this study, we propose a unified and model-agnostic inferential approach for a wide class of partially identified estimands, based on duality theory for optimal transport problems. In randomized experiments, our approach can wrap around any estimates of the conditional distributions and provide uniformly valid inference, even if the initial estimates are arbitrarily inaccurate. Also, our approach is doubly robust in observational studies. Notably, this property allows analysts to use the multiplier bootstrap to select covariates and models without sacrificing validity even if the true model is not included. Furthermore, if the conditional distributions are estimated at semiparametric rates, our approach matches the performance of an oracle with perfect knowledge of the outcome model. Finally, we propose an efficient computational framework, enabling implementation on many practical problems in causal inference.  ( 2 min )

Motivated by the computational difficulties incurred by popular deep learning algorithms for the generative modeling of temporal densities, we propose a cheap alternative which requires minimal hyperparameter tuning and scales favorably to high dimensional problems. In particular, we use a projection-based optimal transport solver [Meng et al., 2019] to join successive samples and subsequently use transport splines [Chewi et al., 2020] to interpolate the evolving density. When the sampling frequency is sufficiently high, the optimal maps are close to the identity and are thus computationally efficient to compute. Moreover, the training process is highly parallelizable as all optimal maps are independent and can thus be learned simultaneously. Finally, the approach is based solely on numerical linear algebra rather than minimizing a nonconvex objective function, allowing us to easily analyze and control the algorithm. We present several numerical experiments on both synthetic and real-world datasets to demonstrate the efficiency of our method. In particular, these experiments show that the proposed approach is highly competitive compared with state-of-the-art normalizing flows conditioned on time across a wide range of dimensionalities.  ( 3 min )

We consider online reinforcement learning (RL) in episodic Markov decision processes (MDPs) under the linear $q^\pi$-realizability assumption, where it is assumed that the action-values of all policies can be expressed as linear functions of state-action features. This class is known to be more general than linear MDPs, where the transition kernel and the reward function are assumed to be linear functions of the feature vectors. As our first contribution, we show that the difference between the two classes is the presence of states in linearly $q^\pi$-realizable MDPs where for any policy, all the actions have approximately equal values, and skipping over these states by following an arbitrarily fixed policy in those states transforms the problem to a linear MDP. Based on this observation, we derive a novel (computationally inefficient) learning algorithm for linearly $q^\pi$-realizable MDPs that simultaneously learns what states should be skipped over and runs another learning algorithm on the linear MDP hidden in the problem. The method returns an $\epsilon$-optimal policy after $\text{polylog}(H, d)/\epsilon^2$ interactions with the MDP, where $H$ is the time horizon and $d$ is the dimension of the feature vectors, giving the first polynomial-sample-complexity online RL algorithm for this setting. The results are proved for the misspecified case, where the sample complexity is shown to degrade gracefully with the misspecification error.  ( 3 min )

Regression on manifolds, and, more broadly, statistics on manifolds, has garnered significant importance in recent years due to the vast number of applications for this type of data. Circular data is a classic example, but so is data in the space of covariance matrices, data on the Grassmannian manifold obtained as a result of principal component analysis, among many others. In this work we investigate prediction sets for regression scenarios when the response variable, denoted by $Y$, resides in a manifold, and the covariable, denoted by X, lies in Euclidean space. This extends the concepts delineated in [Lei and Wasserman, 2014] to this novel context. Aligning with traditional principles in conformal inference, these prediction sets are distribution-free, indicating that no specific assumptions are imposed on the joint distribution of $(X, Y)$, and they maintain a non-parametric character. We prove the asymptotic almost sure convergence of the empirical version of these regions on the manifold to their population counterparts. The efficiency of this method is shown through a comprehensive simulation study and an analysis involving real-world data.  ( 2 min )

Maximum-type statistics of certain functions of the sample covariance matrix of high-dimensional vector time series are studied to statistically confirm or reject the null hypothesis that a data set has been collected under normal conditions. The approach generalizes the case of the maximal deviation of the sample autocovariances function from its assumed values. Within a linear time series framework it is shown that Gumbel-type extreme value asymptotics holds true. As applications we discuss long-only mimimal-variance portfolio optimization and subportfolio analysis with respect to idiosyncratic risks, ETF index tracking by sparse tracking portfolios, convolutional deep learners for image analysis and the analysis of array-of-sensors data.  ( 2 min )

The growing interest in machine learning problems over graphs with additional node information such as texts, images, or labels has popularized methods that require the costly operation of processing the entire graph. Yet, little effort has been made to the development of fast local methods (i.e. without accessing the entire graph) that extract useful information from such data. To that end, we propose a study of local graph clustering using noisy node labels as a proxy for additional node information. In this setting, nodes receive initial binary labels based on cluster affiliation: 1 if they belong to the target cluster and 0 otherwise. Subsequently, a fraction of these labels is flipped. We investigate the benefits of incorporating noisy labels for local graph clustering. By constructing a weighted graph with such labels, we study the performance of graph diffusion-based local clustering method on both the original and the weighted graphs. From a theoretical perspective, we consider recovering an unknown target cluster with a single seed node in a random graph with independent noisy node labels. We provide sufficient conditions on the label noise under which, with high probability, using diffusion in the weighted graph yields a more accurate recovery of the target cluster. This approach proves more effective than using the given labels alone or using diffusion in the label-free original graph. Empirically, we show that reliable node labels can be obtained with just a few samples from an attributed graph. Moreover, utilizing these labels via diffusion in the weighted graph leads to significantly better local clustering performance across several real-world datasets, improving F1 scores by up to 13%.  ( 3 min )

Large language models such as ChatGPT are increasingly explored in medical domains. However, the absence of standard guidelines for performance evaluation has led to methodological inconsistencies. This study aims to summarize the available evidence on evaluating ChatGPT's performance in medicine and provide direction for future research. We searched ten medical literature databases on June 15, 2023, using the keyword "ChatGPT". A total of 3520 articles were identified, of which 60 were reviewed and summarized in this paper and 17 were included in the meta-analysis. The analysis showed that ChatGPT displayed an overall integrated accuracy of 56% (95% CI: 51%-60%, I2 = 87%) in addressing medical queries. However, the studies varied in question resource, question-asking process, and evaluation metrics. Moreover, many studies failed to report methodological details, including the version of ChatGPT and whether each question was used independently or repeatedly. Our findings revealed that although ChatGPT demonstrated considerable potential for application in healthcare, the heterogeneity of the studies and insufficient reporting may affect the reliability of these results. Further well-designed studies with comprehensive and transparent reporting are needed to evaluate ChatGPT's performance in medicine.  ( 2 min )

Multiple data views measured on the same set of participants is becoming more common and has the potential to deepen our understanding of many complex diseases by analyzing these different views simultaneously. Equally important, many of these complex diseases show evidence of subgroup heterogeneity (e.g., by sex or race). HIP (Heterogeneity in Integration and Prediction) is among the first methods proposed to integrate multiple data views while also accounting for subgroup heterogeneity to identify common and subgroup-specific markers of a particular disease. However, HIP is applicable to continuous outcomes and requires programming expertise by the user. Here we propose extensions to HIP that accommodate multi-class, Poisson, and Zero-Inflated Poisson outcomes while retaining the benefits of HIP. Additionally, we introduce an R Shiny application, accessible on shinyapps.io at https://multi-viewlearn.shinyapps.io/HIP_ShinyApp/, that provides an interface with the Python implementation of HIP to allow more researchers to use the method anywhere and on any device. We applied HIP to identify genes and proteins common and specific to males and females that are associated with exacerbation frequency. Although some of the identified genes and proteins show evidence of a relationship with chronic obstructive pulmonary disease (COPD) in existing literature, others may be candidates for future research investigating their relationship with COPD. We demonstrate the use of the Shiny application with a publicly available data. An R-package for HIP would be made available at https://github.com/lasandrall/HIP.  ( 3 min )

Lion (Evolved Sign Momentum), a new optimizer discovered through program search, has shown promising results in training large AI models. It performs comparably or favorably to AdamW but with greater memory efficiency. As we can expect from the results of a random search program, Lion incorporates elements from several existing algorithms, including signed momentum, decoupled weight decay, Polak, and Nesterov momentum, but does not fit into any existing category of theoretically grounded optimizers. Thus, even though Lion appears to perform well as a general-purpose optimizer for a wide range of tasks, its theoretical basis remains uncertain. This lack of theoretical clarity limits opportunities to further enhance and expand Lion's efficacy. This work aims to demystify Lion. Based on both continuous-time and discrete-time analysis, we demonstrate that Lion is a theoretically novel and principled approach for minimizing a general loss function $f(x)$ while enforcing a bound constraint $\|x\|_\infty \leq 1/\lambda$. Lion achieves this through the incorporation of decoupled weight decay, where $\lambda$ represents the weight decay coefficient. Our analysis is made possible by the development of a new Lyapunov function for the Lion updates. It applies to a broader family of Lion-$\kappa$ algorithms, where the $\text{sign}(\cdot)$ operator in Lion is replaced by the subgradient of a convex function $\kappa$, leading to the solution of a general composite optimization problem of $\min_x f(x) + \kappa^*(x)$. Our findings provide valuable insights into the dynamics of Lion and pave the way for further improvements and extensions of Lion-related algorithms.  ( 3 min )

Large transformer models pretrained on offline reinforcement learning datasets have demonstrated remarkable in-context reinforcement learning (ICRL) capabilities, where they can make good decisions when prompted with interaction trajectories from unseen environments. However, when and how transformers can be trained to perform ICRL have not been theoretically well-understood. In particular, it is unclear which reinforcement-learning algorithms transformers can perform in context, and how distribution mismatch in offline training data affects the learned algorithms. This paper provides a theoretical framework that analyzes supervised pretraining for ICRL. This includes two recently proposed training methods -- algorithm distillation and decision-pretrained transformers. First, assuming model realizability, we prove the supervised-pretrained transformer will imitate the conditional expectation of the expert algorithm given the observed trajectory. The generalization error will scale with model capacity and a distribution divergence factor between the expert and offline algorithms. Second, we show transformers with ReLU attention can efficiently approximate near-optimal online reinforcement learning algorithms like LinUCB and Thompson sampling for stochastic linear bandits, and UCB-VI for tabular Markov decision processes. This provides the first quantitative analysis of the ICRL capabilities of transformers pretrained from offline trajectories.  ( 2 min )

We consider the variable selection problem for two-sample tests, aiming to select the most informative variables to distinguish samples from two groups. To solve this problem, we propose a framework based on the kernel maximum mean discrepancy (MMD). Our approach seeks a group of variables with a pre-specified size that maximizes the variance-regularized MMD statistics. This formulation also corresponds to the minimization of asymptotic type-II error while controlling type-I error, as studied in the literature. We present mixed-integer programming formulations and develop exact and approximation algorithms with performance guarantees for different choices of kernel functions. Furthermore, we provide a statistical testing power analysis of our proposed framework. Experiment results on synthetic and real datasets demonstrate the superior performance of our approach.  ( 2 min )

We present a simulation strategy for the real-time dynamics of quantum fields, inspired by reinforcement learning. It builds on the complex Langevin approach, which it amends with system specific prior information, a necessary prerequisite to overcome this exceptionally severe sign problem. The optimization process underlying our machine learning approach is made possible by deploying inherently stable solvers of the complex Langevin stochastic process and a novel optimality criterion derived from insight into so-called boundary terms. This conceptual and technical progress allows us to both significantly extend the range of real-time simulations in 1+1d scalar field theory beyond the state-of-the-art and to avoid discretization artifacts that plagued previous real-time field theory simulations. Limitations of and promising future directions are discussed.  ( 2 min )

We consider the problem of model selection in a high-dimensional sparse linear regression model under the differential privacy framework. In particular, we consider the problem of differentially private best subset selection and study its utility guarantee. We adopt the well-known exponential mechanism for selecting the best model, and under a certain margin condition, we establish its strong model recovery property. However, the exponential search space of the exponential mechanism poses a serious computational bottleneck. To overcome this challenge, we propose a Metropolis-Hastings algorithm for the sampling step and establish its polynomial mixing time to its stationary distribution in the problem parameters $n,p$, and $s$. Furthermore, we also establish approximate differential privacy for the final estimates of the Metropolis-Hastings random walk using its mixing property. Finally, we also perform some illustrative simulations that echo the theoretical findings of our main results.  ( 2 min )

This paper studies two fundamental problems in regularized Graphon Mean-Field Games (GMFGs). First, we establish the existence of a Nash Equilibrium (NE) of any $\lambda$-regularized GMFG (for $\lambda\geq 0$). This result relies on weaker conditions than those in previous works for analyzing both unregularized GMFGs ($\lambda=0$) and $\lambda$-regularized MFGs, which are special cases of GMFGs. Second, we propose provably efficient algorithms to learn the NE in weakly monotone GMFGs, motivated by Lasry and Lions [2007]. Previous literature either only analyzed continuous-time algorithms or required extra conditions to analyze discrete-time algorithms. In contrast, we design a discrete-time algorithm and derive its convergence rate solely under weakly monotone conditions. Furthermore, we develop and analyze the action-value function estimation procedure during the online learning process, which is absent from algorithms for monotone GMFGs. This serves as a sub-module in our optimization algorithm. The efficiency of the designed algorithm is corroborated by empirical evaluations.  ( 2 min )

Diffusion models suffer from slow sample generation at inference time. Therefore, developing a principled framework for fast deterministic/stochastic sampling for a broader class of diffusion models is a promising direction. We propose two complementary frameworks for accelerating sample generation in pre-trained models: Conjugate Integrators and Splitting Integrators. Conjugate integrators generalize DDIM, mapping the reverse diffusion dynamics to a more amenable space for sampling. In contrast, splitting-based integrators, commonly used in molecular dynamics, reduce the numerical simulation error by cleverly alternating between numerical updates involving the data and auxiliary variables. After extensively studying these methods empirically and theoretically, we present a hybrid method that leads to the best-reported performance for diffusion models in augmented spaces. Applied to Phase Space Langevin Diffusion [Pandey & Mandt, 2023] on CIFAR-10, our deterministic and stochastic samplers achieve FID scores of 2.11 and 2.36 in only 100 network function evaluations (NFE) as compared to 2.57 and 2.63 for the best-performing baselines, respectively. Our code and model checkpoints will be made publicly available at \url{https://github.com/mandt-lab/PSLD}.  ( 2 min )

Transformers pretrained on diverse tasks exhibit remarkable in-context learning (ICL) capabilities, enabling them to solve unseen tasks solely based on input contexts without adjusting model parameters. In this paper, we study ICL in one of its simplest setups: pretraining a linearly parameterized single-layer linear attention model for linear regression with a Gaussian prior. We establish a statistical task complexity bound for the attention model pretraining, showing that effective pretraining only requires a small number of independent tasks. Furthermore, we prove that the pretrained model closely matches the Bayes optimal algorithm, i.e., optimally tuned ridge regression, by achieving nearly Bayes optimal risk on unseen tasks under a fixed context length. These theoretical findings complement prior experimental research and shed light on the statistical foundations of ICL.  ( 2 min )

Obtaining continuously updated predictions is a major challenge for personalised medicine. Leveraging combinations of parametric regressions and machine learning approaches, the personalised online super learner (POSL) can achieve such dynamic and personalised predictions. We adapt POSL to predict a repeated continuous outcome dynamically and propose a new way to validate such personalised or dynamic prediction models. We illustrate its performance by predicting the convection volume of patients undergoing hemodiafiltration. POSL outperformed its candidate learners with respect to median absolute error, calibration-in-the-large, discrimination, and net benefit. We finally discuss the choices and challenges underlying the use of POSL.  ( 2 min )

Many real-world prediction tasks have outcome variables that have characteristic heavy-tail distributions. Examples include copies of books sold, auction prices of art pieces, demand for commodities in warehouses, etc. By learning heavy-tailed distributions, "big and rare" instances (e.g., the best-sellers) will have accurate predictions. Most existing approaches are not dedicated to learning heavy-tailed distribution; thus, they heavily under-predict such instances. To tackle this problem, we introduce Learning to Place (L2P), which exploits the pairwise relationships between instances for learning. In its training phase, L2P learns a pairwise preference classifier: is instance A > instance B? In its placing phase, L2P obtains a prediction by placing the new instance among the known instances. Based on its placement, the new instance is then assigned a value for its outcome variable. Experiments on real data show that L2P outperforms competing approaches in terms of accuracy and ability to reproduce heavy-tailed outcome distribution. In addition, L2P provides an interpretable model by placing each predicted instance in relation to its comparable neighbors. Interpretable models are highly desirable when lives and treasure are at stake.  ( 3 min )

Across a wide array of disciplines, many researchers use machine learning (ML) algorithms to identify a subgroup of individuals, called exceptional responders, who are likely to be helped by a treatment the most. A common approach consists of two steps. One first estimates the conditional average treatment effect or its proxy using an ML algorithm. They then determine the cutoff of the resulting treatment prioritization score to select those predicted to benefit most from the treatment. Unfortunately, these estimated treatment prioritization scores are often biased and noisy. Furthermore, utilizing the same data to both choose a cutoff value and estimate the average treatment effect among the selected individuals suffer from a multiple testing problem. To address these challenges, we develop a uniform confidence band for experimentally evaluating the sorted average treatment effect (GATES) among the individuals whose treatment prioritization score is at least as high as any given quantile value, regardless of how the quantile is chosen. This provides a statistical guarantee that the GATES for the selected subgroup exceeds a certain threshold. The validity of the proposed methodology depends solely on randomization of treatment and random sampling of units without requiring modeling assumptions or resampling methods. This widens its applicability including a wide range of other causal quantities. A simulation study shows that the empirical coverage of the proposed uniform confidence bands is close to the nominal coverage when the sample is as small as 100. We analyze a clinical trial of late-stage prostate cancer and find a relatively large proportion of exceptional responders with a statistical performance guarantee.  ( 3 min )

Covariate shift occurs prevalently in practice, where the input distributions of the source and target data are substantially different. Despite its practical importance in various learning problems, most of the existing methods only focus on some specific learning tasks and are not well validated theoretically and numerically. To tackle this problem, we propose a unified analysis of general nonparametric methods in a reproducing kernel Hilbert space (RKHS) under covariate shift. Our theoretical results are established for a general loss belonging to a rich loss function family, which includes many commonly used methods as special cases, such as mean regression, quantile regression, likelihood-based classification, and margin-based classification. Two types of covariate shift problems are the focus of this paper and the sharp convergence rates are established for a general loss function to provide a unified theoretical analysis, which concurs with the optimal results in literature where the squared loss is used. Extensive numerical studies on synthetic and real examples confirm our theoretical findings and further illustrate the effectiveness of our proposed method.  ( 2 min )

We characterize the statistical efficiency of knowledge transfer through $n$ samples from a teacher to a probabilistic student classifier with input space $\mathcal S$ over labels $\mathcal A$. We show that privileged information at three progressive levels accelerates the transfer. At the first level, only samples with hard labels are known, via which the maximum likelihood estimator attains the minimax rate $\sqrt{{|{\mathcal S}||{\mathcal A}|}/{n}}$. The second level has the teacher probabilities of sampled labels available in addition, which turns out to boost the convergence rate lower bound to ${{|{\mathcal S}||{\mathcal A}|}/{n}}$. However, under this second data acquisition protocol, minimizing a naive adaptation of the cross-entropy loss results in an asymptotically biased student. We overcome this limitation and achieve the fundamental limit by using a novel empirical variant of the squared error logit loss. The third level further equips the student with the soft labels (complete logits) on ${\mathcal A}$ given every sampled input, thereby provably enables the student to enjoy a rate ${|{\mathcal S}|}/{n}$ free of $|{\mathcal A}|$. We find any Kullback-Leibler divergence minimizer to be optimal in the last case. Numerical simulations distinguish the four learners and corroborate our theory.  ( 2 min )

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News provided by aibrews.com Researchers present LLark: A Multimodal Foundation Model for Music - an open-source instruction-tuned multimodal model for music understanding. LLark is trained entirely from open-source music data and models [Demo | Paper] Researchers released LLaVA-1.5. LLaVA (Large Language and Vision Assistant) is an open-source large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. LLaVA-1.5 achieved SoTA on 11 benchmarks, with just simple modifications to the original LLaVA and completed training in ~1 day on a single 8-A100 node [Demo | Paper | GitHub]. Voice AI platform ElevenLabs released AI Dubbing tool that enables users to automatically translate any audio in a video into a different language whil…

The rapid growth of artificial intelligence (AI) could lead to a significant increase in global electricity consumption, according to a peer-reviewed analysis published in Joule. The analysis estimates that if current trends continue, AI could drive the demand for electricity in data centers to consume at least 85.4 terawatt-hours annually, which is more than what many small countries use in a year. AI is energy-intensive, with both the training and inference phases requiring a significant amount of energy. The size of AI models, such as large language models, and the location of data centers also contribute to energy usage. Factors such as cooling requirements and the type of hardware used can impact energy consumption. Source : https://www.scientificamerican.com/article/the-ai-boom-could-use-a-shocking-amount-of-electricity/ submitted by /u/NuseAI [link] [comments]

Today's conversational bots like Claude and GPT can chat impressively but aren't great at complex planning or executing technical tasks. To overcome this, new research from HKU builds open-source AI agents that blend natural language and coding skills. They're called Lemur and Lemur-Chat. The researchers think achieving versatile real-world agents requires models that integrate both fluid natural language abilities and precise programming language control. Humans combine plain speech for higher-level goals with languages like Python when we need to plan intricately and execute exactly. AI needs both capacities too. But most existing models specialize in pure language or pure code. There's a separation that is limiting. The team created Lemur by pretraining the open-source Llama-2 on a massive mixed corpus with 10x more natural language than code. This improved its programming abilities while retaining conversational strength. Further instruction tuning optimized Lemur-Chat for following free-form directions in language. Experiments found Lemur surpassed specialized coding-only models like Codex in overall benchmarks. Lemur-Chat then exceeded Lemur by 15% after instruction tuning. More importantly, Lemur-Chat won 12/13 new "agent tests" designed to mimic real-world challenges needing both language and programming prowess. It beat alternatives at: Using tools like Python and Wikipedia to enhance reasoning Debugging code by leveraging error messages Improving the most from natural language feedback Exploring partially observable environments like cybersecurity and web browsing simulations. Lemur-Chat matched GPT-3.5 in many tests, closing the gap between commercial and open-source agents. TLDR: New open-source AI agents combine coding and language skills. Experiments show the combo unlocks more performance across technical challenges. Full summary is here. Paper is here. submitted by /u/Successful-Western27 [link] [comments]

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In a recent article published in the journal Nature, researchers developed AI Tool EVEscape, a tool to forecast which severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) strains have the highest potential to escape host immunity.[1] Microsoft seems to be working on the possible development of an artificial intelligence (AI) system that can understand and resolve customer support requests using natural language processing.[2] Google’s Search Generative Experience (SGE) will let you create images right from a text prompt starting Thursday.[3] The Biden administration is considering closing a loophole that gives Chinese companies access to American artificial intelligence (AI) chips through units located overseas, according to four people familiar with the matter.[4] Sources: [1] https://www.news-medical.net/news/20231012/EVScape-New-tool-to-forecast-which-SARS-CoV-2-variants-could-dodge-our-immunity.aspx [2] https://winbuzzer.com/2023/10/11/microsoft-gears-up-for-a-revolutionary-natural-language-customer-support-ai-xcxwbn/ [3] https://www.theverge.com/2023/10/12/23913337/google-ai-powered-search-sge-images-written-drafts [4] https://www.reuters.com/technology/biden-eyes-adding-ai-chip-curbs-chinese-companies-abroad-2023-10-13/ submitted by /u/Excellent-Target-847 [link] [comments]

Also if anyone has anyone who should be a voice actor included in it it can also clone voices. Idk I need to make sure it works for a wide variety of books. As long as they don’t use ‘ for quotes cause the computer getts that confused when “ I’ve “ and such uses the same symbol submitted by /u/Impossible_Belt_7757 [link] [comments]

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ClassifyML is an application for creating specialised image datasets for use with an ML training algorithm. Simply import your chosen images into the app via file manager, drag'n'drop or the on device camera and create your bounding boxes and then export your images and JSON into a structured folder. LINK: https://apps.apple.com/app/classify-ml/id6461013113 https://preview.redd.it/dicsq9d3k1ub1.png?width=313&format=png&auto=webp&s=7976a61f599c658d948dec12db0b8ec93274ad93 https://preview.redd.it/3tswxdd3k1ub1.png?width=313&format=png&auto=webp&s=56ca30546984402f4dbba628b73732918e921758 https://preview.redd.it/y0xelmz3k1ub1.png?width=313&format=png&auto=webp&s=a755ea61bc247c6aacb61a31c700e4e80a1ed69f submitted by /u/LiamRogers99 [link] [comments]  ( 9 min )

Recently I came across Open AI's Spinning Up Project, which seems to be well structured, but quite introductory. What are some resources you use for learning RL? submitted by /u/OwnAd9305 [link] [comments]  ( 9 min )

Hello, I'm looking for an open source LLM that can extract all the characters from an inputted book, and isolate passages with descriptive writing that involves imagery. Can anyone suggest me something? Thanks! submitted by /u/slomorosh [link] [comments]  ( 9 min )

In this blog, we explore different definitions of “the edge,” and understand the factors driving AI/ML to the edge. We examine why the trends of LLMs and edge computing are intersecting now, and how teams can take advantage of their combined power today. We also demonstrate how LLMs can be used in an edge environment to generate insights for a real-world use case today. Consider a geologist working in a remote oil field who is responsible for building and analyzing 3D models of oil fields to determine production capacity and the impact on profitability. In this demo, we walk through how Code Llama, Chassisml.io, and Modzy could be used to build a dashboard that geologists could use to analyze well data in real-time in a remote, network restricted environment, allowing for LLM insights generated at the edge. Learn more: https://www.modzy.com/modzy-blog/deploy-and-run-llms-at-the-edge submitted by /u/modzykirsten [link] [comments]  ( 9 min )

https://openreview.net/group?id=ICLR.cc/2024/Conference submitted by /u/_puhsu [link] [comments]  ( 8 min )

I am running AutoTokenizer from transformers on vscode. The vscode crashes showing error and not responding. I don't understand what's wrong. submitted by /u/ArtichokeOne5897 [link] [comments]  ( 8 min )

Hey Guys, ​ I am currently spearheading a project for a client in the insurance industry, with a primary objective being the digitalization of thousands of hardcopy contracts. The ultimate goal is to automatically extract particular information from these newly digital documents, namely "date", "insurance premium", "insurance type", and "contractor's name". However, I anticipate a level of variability in terms of exact terminology used, particularly with regards to "insurance premium" and "insurance type". (There is no handwritten text) ​ I am keen on sharing the methodology I intend to apply for this project and invite your invaluable feedback and suggestions: ​ - Firstly, I'll execute the scanning/digitalization of the documents manually. - Post this, I plan to utilize Tesseract in combination with Python for the extraction of text from the preprocessed images. - I am considering using libraries such as NLTK or spaCy to preprocess this text (this will involve steps like lower casing, removing punctuations, etc.) - Finally, I plan to train a custom model for Named Entity Recognition (NER), to accommodate the potential semantic variations in entity labeling which are specific to entities like "insurance premium" and "insurance type". ​ I would be immensely grateful if I could gain your insights on the above-proposed pipeline - Are there any glaring pitfalls I need to avoid or perhaps some improvements that I could incorporate? Your expert advice can certainly help ensure the success of this venture. ​ Many thanks in anticipation for your time and valuable inputs! submitted by /u/Background_Thanks604 [link] [comments]  ( 9 min )

I work for this database company SingleStore and we are hosting a AI & ML conference in San Francisco on 17th of October, 2023. It is an in-person conference with amazing speakers line-up like Harrison Chase, co-founder and CEO of LangChain and many more. We will have hands-on workshops, swags giveaway and much more. I don't know if it makes sense to share this but I believe it might help some of you near San Francisco to go and meet the industry leaders and network with other data engineering folks. Use my discount coupon code 'PAVAN100OFF' to avail 100% off on the ticket price. (the original ticket price is $199) Get your tickets now! submitted by /u/PavanBelagatti [link] [comments]  ( 9 min )

Has anyone ever attempted this or finetuning before on the CoreML version? I’m currently trying to and I’m not even sure where to start tbh. CoreML version of Llama 2: https://huggingface.co/coreml-projects/Llama-2-7b-chat-coreml submitted by /u/Inside-Aromatic [link] [comments]  ( 9 min )

Hi all, I’m studying the concepts of machine learning. However, I am stuck because I still don’t see how introducing a penalty using lasso regression can drive some parameter coefficients to zero. When doing the calculations, I only get the final value (ordinary least squares + penalty) and don’t directly see a coefficient value being reduced. I've looked at many materials and resources trying to explain this, but I still can't see how it's done. I think the important thing for me is seeing it going to zero or, at the very least, seeing it during calculation. Is there anyone that can help explain this better? Or, If you know of a formula that I can derive that, during the derivation process, shows a coefficient being reduced or set to zero, that would also help. Also, any good resources on the topic would be appreciated. Edit: This post should have been posted in r/learnmachinelearning here is a link to the same post in that subreddit submitted by /u/thismymind [link] [comments]  ( 9 min )

I am an academic and I have some funding. However, I cannot just plug in my lab card with a recurrent payment, procedures don't allow it. Is there a way to "top up" some compute credits on the OpenAI accounts ? Is anyone having the same problem ? Thanks. submitted by /u/Jean-Porte [link] [comments]  ( 9 min )

Hi, we are working on a project that involves dealing with messy automotive data, and are looking for guidance on possible approaches and tools. We aim to map messy supplier data of car makes/models to standardized values from our approved list. This requires handling various challenges like typos, varied specificity, and sometimes research-based mapping (e.g., using engine size and production year to ascertain a chassis code). eg: If a supplier provides 'BNW 316i saloon 1990-1994', (typo intentional) we would like to match it to our standardized value of 'BMW 3 Series (E36)'. Our old approach has been a combination of utilizing fuzzy matching for typos/basic matching and time consuming manual processing and verification. We have recently experimented with using GPT for providing guess…  ( 10 min )

Today's conversational bots like Claude and GPT can chat impressively but aren't great at complex planning or executing technical tasks. To overcome this, new research from HKU builds open-source AI agents that blend natural language and coding skills. They're called Lemur and Lemur-Chat. The researchers think achieving versatile real-world agents requires models that integrate both fluid natural language abilities and precise programming language control. Humans combine plain speech for higher-level goals with languages like Python when we need to plan intricately and execute exactly. AI needs both capacities too. But most existing models specialize in pure language or pure code. There's a separation that is limiting. The team created Lemur by pretraining the open-source Llama-2 on a massive mixed corpus with 10x more natural language than code. This improved its programming abilities while retaining conversational strength. Further instruction tuning optimized Lemur-Chat for following free-form directions in language. Experiments found Lemur surpassed specialized coding-only models like Codex in overall benchmarks. Lemur-Chat then exceeded Lemur by 15% after instruction tuning. More importantly, Lemur-Chat won 12/13 new "agent tests" designed to mimic real-world challenges needing both language and programming prowess. It beat alternatives at: Using tools like Python and Wikipedia to enhance reasoning Debugging code by leveraging error messages Improving the most from natural language feedback Exploring partially observable environments like cybersecurity and web browsing simulations. Lemur-Chat matched GPT-3.5 in many tests, closing the gap between commercial and open-source agents. TLDR: New open-source AI agents combine coding and language skills. Experiments show the combo unlocks more performance across technical challenges. Full summary is here. Paper is here. submitted by /u/Successful-Western27 [link] [comments]  ( 9 min )

Hi, we've just released a new version of AgileRL, our evolutionary hyperparameter optimisation framework built for RL that is 10x faster than SOTA. We've introduced PPO, Rainbow DQN, some sophisticated replay buffers, and also collaborated with the Farama Foundation to create some tutorials (more on the way). Please check it out and take it for a spin. We're also looking for contributors so get in touch if you would like to be involved! https://github.com/AgileRL/AgileRL submitted by /u/nicku_a [link] [comments]  ( 9 min )

Hi everyone, I’m one of the creators of Evidently, an open-source (Apache 2.0) tool for production ML monitoring. We’ve just launched a free open course on ML observability that I wanted to share with the community. The course covers: 📚 Key concepts of ML monitoring and observability (data drift, data and model quality metrics, etc.) 🔡 Monitoring unstructured data (embeddings, texts, LLMs, etc.) 🛠 Different deployment architectures (batch ML monitoring jobs, near real-time ML monitoring, etc.) The course is free and open. All materials are public, with no sign-up required. You’ll work with open-source tools like Evidently, MLflow, Airflow, and Grafana. We’ve already published the first 12 videos with notes and code examples. We’ll add new lessons and deployment blueprints over the following weeks. The official course start date is October 16, 2023. You can also learn at your own pace. Course info and notes: https://learn.evidentlyai.com/ [Background] We’ve been working on Evidently since late 2020 and have spoken to 100s of data scientists, ML engineers, and ML platform teams in different industries. In this course, we tried to sum up answers to the frequent questions on the topic. It starts with high-level theoretical modules and goes to complete deployment blueprints. It is approachable for different levels of knowledge, and you can pick only the modules you are interested in. Looking forward to meeting you at the course! submitted by /u/mllena [link] [comments]  ( 9 min )

I am trying to do machine learning in ArcPro, and I want to understand the relationship between x, y, numeric variable 1, numeric variable 2, and one nominal variable (classified; i.e. can be one of four values). I'd like to be able to predict numeric variable 1 based on everything else. Can ArcPro accommodate machine learning for anything other than raster type data. That is, can it be used to do machine learning on point (numeric) data? Thanks! submitted by /u/arcgis_123 [link] [comments]  ( 9 min )

In 2023, Transformers made significant breakthroughs in time-series forecasting For example, earlier this year, Zalando proved that scaling laws apply in time-series as well. Providing you have large datasets ( And yes, 100,000 time series of M4 are not enough - smallest 7B Llama was trained on 1 trillion tokens! ) Nixtla curated a 100B dataset of time-series and trained TimeGPT, the first foundation model on time-series. The results are unlike anything we have seen so far. I published the results in my latest article. I hope the research will be insightful for people who work on time-series projects. Link: https://aihorizonforecast.substack.com/p/timegpt-the-first-foundation-model Note: If you know any other good resources on very large benchmarks for time series models, feel free to add them below. ​ submitted by /u/nkafr [link] [comments]

Hi everyone. I am aware that there is a plethora of deep generative models out there (e.g. variational autoencoders (VAE), GANs) that can model high-dimensional data as the images of latent variables under a non-linear mapping (typically neural network). In more traditional methods such as probabilistic PCA, the latent variables can be marginalised analytically. In Bayesian PCA (BPCA), we can additionally integrate out the linear mapping, from the latent space to the observation space, by adopting the variational lower bound that leads to closed form updates of the parameters. The Gaussian Process Latent Variable (GPLVM) model adopts a non-linear probabilistic mapping (a Gaussian process) that can be marginalised. These two models enjoy to a certain degree analytical solutions concerning the inference of the latent variables and the mapping. I have been wondering whether there is any research into more "complex" models (perhaps I should call them deep) that are capable of modelling more complex data distributions than the GPVLM and BPCA, but retain analytical solutions when inferring the posterior of the latent variables (like BPCA) or the mapping (like GPLVM)? What I like about the GPLVM and BPCA is that they possess an objective function (i.e. ELBO) that can be analytically optimised, as opposed to the intractable objective of VAEs that necessitates Monte-Carlo averages and stochastic gradient. Could somebody please point me to such examples of more complex generative models that admit analytical inference for working out the posterior of the latent variables or the mapping? ----- This has also been posted on stack exchange: https://ai.stackexchange.com/q/42418/61537 submitted by /u/ngiann [link] [comments]  ( 9 min )

Do I love teaching? Oh, absolutely, YES a big YES! My time as a TA for countless semesters has been amazing. Staying after hours, spending long evenings and early mornings, to make each of my students find ease in debugging both easy-peasy and mind-boggling programs – it’s been a joy, truly. Watching those fresh faces, whom I introduced to Python in their first year ( intro to programming lab), now immerse themselves into my computer vision labs, exploring computer vision and deep learning in their third/forth year – it’s incredibly rewarding! And yeah my students kind of like me! after each semester I get tons of emails thanking me and my TAship review is always good. But, ugh, do I have enough publications to become faculty? A big fat NO! My efforts have been relentless, and everyone in my department would nod in agreement. But luck and reviewers? Not my best pals, apparently. So yeah, I don’t have a stack of 8 top-tier papers. I’ve managed to scrape together 3, and a few second tiers. My citation count is not that bad somewhere between 200 and 300-ish. Now, what’s next for me? Dive into the industry? become a high school teacher? Or perhaps, do a postdoc journey, fingers crossed for a sprinkle more luck and few more papers? Edit: This doesn't mean I don't like research, I actually love it too, I have done quite a few internship in quite big companies, most of the time they extend my intership and I even got publication out of one in 5 month. But I just like to teach a lot! strangely I got social anxiety every where other than my classrooms/labs. submitted by /u/LongjumpingSchool646 [link] [comments]  ( 9 min )

I'm seeing some architectures come out from the LLM world that probably wouldn't survive the trip to production. If you choose a vector database how will you handle your other database needs? Then you'll need 2 databases. https://bionic-gpt.com/blog/you-dont-need-a-vector-database/ submitted by /u/purton_i [link] [comments]  ( 9 min )

In the original paper of MoCo, it said that: Using a queue can make the dictionary large, but it also makes it intractable to update the key encoder by back-propagation (the gradient should propagate to all samples in the queue). First I thought that the main reason that the bp cannot imply on key encoder is that the queue operation is not differentable. But It seems not true. You can compute the gradient of all samples in the queue, then bp should be performed properly. See the code at the bottom. So WHAT IS THE REAL REASON THAT THE BP IS INTRACTABLE FOR KEY ENCODER? In my opinion, I think may be because of the large size of the queue (dictionary) which makes the memory explosive. python q = nn.Linear(768,128) k = nn.Linear(768,128) bs = 64 ks = 4095 model = nn.ModuleList([q,k]) x = torch.randn(bs, 768) optim = torch.optim.SGD(model.parameters(),lr=0.01) loss = nn.CrossEntropyLoss() def forward(x): xq = q(x) xk = k(x + 0.1) que = torch.rand(ks,128) pos = torch.einsum("nc,nc->n",xq,xk) neg = torch.einsum("nc,kc->nk",xq,que) out = torch.cat([pos.unsqueeze(-1),neg],dim=1) t = torch.zeros(out.shape[0],dtype=torch.long) l = loss(out,t) return l loss = forward(x) loss.backward() optim.step() submitted by /u/whishtLF [link] [comments]  ( 9 min )

A senior phd student at a moderately famous university. I have a reasonable number of accepted papers as first author in tier-1 conferences. I was thinking of going into academia, so recently I started proposing many ideas to my advisor so that I can mentor some junior students. However my advisor is rejecting every idea I suggest saying it won’t work. I’m feeling very dejected and I feel like I should give up going into academia. I don’t know what I’m expecting from here. Is your advisor like this too? submitted by /u/mildlyphd [link] [comments]  ( 9 min )

Posted by Han Zhou, Student Researcher, and Subhrajit Roy, Senior Research Scientist, Google Research Prompting large language models (LLMs) has become an efficient learning paradigm for adapting LLMs to a new task by conditioning on human-designed instructions. The remarkable in-context learning (ICL) ability of LLMs also leads to efficient few-shot learners that can generalize from few-shot input-label pairs. However, the predictions of LLMs are highly sensitive and even biased to the choice of templates, label spaces (such as yes/no, true/false, correct/incorrect), and demonstration examples, resulting in unexpected performance degradation and barriers for pursuing robust LLM applications. To address this problem, calibration methods have been developed to mitigate the effects of t…  ( 93 min )

Artificial Intelligence (AI) is emerging as a formidable force, revolutionizing how we conceive, create, and deliver software solutions. As technology advances at an unprecedented pace, the role of AI in this domain has become increasingly significant. It’s no longer just a buzzword; it’s a fundamental tool that promises to reshape the entire software development process.… Read More »Significance of AI in the development of software products The post Significance of AI in the development of software products appeared first on Data Science Central.  ( 19 min )

Companies, more often, pay attention to automation and innovation over proficiency and productivity. However, firms can maintain a balance between both due to the extensive usage of AI and data science programs. Here are the stats that show the impact of AI and data science in diverse sectors: Applications of AI and data science have… Read More »Future of AI and data science – How to secure a bright career The post Future of AI and data science – How to secure a bright career appeared first on Data Science Central.  ( 21 min )

What are the ways to create plasticity in neural network? Without using weights,bias and activation functions? submitted by /u/Sith_vader3 [link] [comments]  ( 8 min )

Hi ! My group (4 people) has chosen to make an application that translates ancient stone inscriptions to modern languages as our university project . We can use external libraries to process images that we are going to translate but as we understood we have to build the neural network ourselves from scratch. My questions are 1) is this possible to do within 10 months? 2) if so how would you approach it ? submitted by /u/sakith123 [link] [comments]

At SHoP Architects, a New York City-based architectural firm, Mengyi Fan and her team aim to inspire industry professionals to create visual masterpieces by incorporating emerging technologies. Fan, the director of visualization at SHoP, has expertise that spans the fields of architectural visualization and design. She takes a definitive, novel and enduring approach to designing Read article >  ( 6 min )

Hi, we've just released a new version of AgileRL, our evolutionary hyperparameter optimisation framework built for RL that is 10x faster than SOTA. We've introduced PPO, Rainbow DQN, some sophisticated replay buffers, and also collaborated with the Farama Foundation to create some tutorials (more on the way). Please check it out and take it for a spin. We're also looking for contributors so get in touch if you would like to be involved! https://github.com/AgileRL/AgileRL submitted by /u/nicku_a [link] [comments]

I am currently dealing with an environment, that most of the time (90% of all state transitions) clips the action selected from the agent. Sometimes even down to the point where the action selected by the agent is completly ignored. This causes a lot of problems, because for example the entropy bonus does not works, since the agent learns to select any action, when it doesn't matter anyway but selects the same action (low entropy) when the actions have an effect. Using the PPO algorithm I was thinking of masking the state transitions in the policy updates, according to how much the action was clipped in the environment. And I thought V(s) should be masked, because it can still learn from the state transitions even if the action was effectively ignored by the environment. submitted by /u/flxh13 [link] [comments]

I'm training some agents using fairly vanilla PPO on a hand-made environment. These agents learn to perform the task pretty well, but while I was examining their action probabilities during an evaluation episode, I had the idea to turn off deterministic action selection. To my surprise, allowing probabilistic action selection (as opposed to argmax action selection) actually improved performance in some cases. I had always thought that deterministic actions during evaluation was fairly standard, but now am thinking that maybe I missed something and that there are cases where you wouldn't want determinism? My question is: how common is it actually to use deterministic actions vs. probabilistic ones at evaluation time, and does anyone know of studies/papers/examples where the authors found probabilistic evaluation to outperform determinism? submitted by /u/Impallion [link] [comments]

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Hey, So I am working on reinforcement learning package in C# (currently under heavy development): https://github.com/asieradzk/RL_Matrix My goal is to create something superior to unity's ML Agents for Godot to democratize access to reinforcement learning for people (without having them know what a tensor is) So far I've added some barebones DQN and PPO that (only output single discrete action) as proof of concept to test my code architecture. So I am going through the daunting task of having some universal workflow for setting up environments. For any shape observations and any count actions, both discrete and continuous. As I am finishing my multi-head multi-action output I've come to realise that there are many possible architectures I could setup multi head outputs, for instanc…

To my knowledge, when using the Q Learning family algorithm, we must know the next state as well as the action spaces in couple with that observation in order to evaluate the reward for the next state with the target network. But I have some problem when trying to define this next state in turn turn-based game in which the agent have to make a certain number of actions and then wait for the opponent to do some actions before it can interact with the environment again. We can take Hearthstone as an example that each player have to wait for other to play a number of cards before can take any action. Currently, I have two options for this: - Treat the environment right after the agent's turn ended, which will lack the action space. - Treat the environment just before the agent's turn begins, which will have all the actions available that it can choose from but this will make the agent's last action very noisy. That state could be a good state if the opponent playing badly or they are very good and make our last decision seem like a very bad choice. Thanks in advance for any suggestions. If my problem is a common task that others have already solved many times, I will be very thankful for that keyword. submitted by /u/No-Concentrate-6037 [link] [comments]

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How does the neural network process input that were same but shown different to the network model? submitted by /u/Sith_vader3 [link] [comments]

i gave chatgpt vision an illustration of neural network from The Principles of Deep Learning Theory. what to know how correct its reponse is here is the response: https://preview.redd.it/inqe5xukxptb1.png?width=453&format=png&auto=webp&s=6e1079baeae8235b0e03a677e4006d1077af36a8 submitted by /u/YeshwanthRam [link] [comments]

Artificial intelligence (AI) can provide "machine usefulness" for human workers, augmenting their jobs rather than replacing them. However, there is a concern that AI could lead to job displacement and reinforce economic inequality. MIT economist Daron Acemoglu emphasizes the importance of making AI more useful to humans and ensuring that the economic benefits are shared widely. He suggests that innovations that augment workers' tasks can lead to prosperity for the workforce. Acemoglu also highlights the need for worker power and the careful implementation of technology to achieve shared prosperity and productivity gains. Source : https://idss.mit.edu/news/who-will-benefit-from-ai/ submitted by /u/NuseAI [link] [comments]

I just need three things for it: It must be knowledgeable about things, such as physics, math, hystory, books, geography, etc. It also must be original, with a high level of SEO and AI detection score. It must be available in Italy. The last part is essential. Claude 2 is very famous but with sms verification from usa (which I don't have and I don't want to give credit card info/pay to have) it's made almost impossible even with vpn. submitted by /u/luigirovatti1 [link] [comments]

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Creating true, enduring, lasting peace on Earth is an ambitious and complex endeavor that requires multifaceted approaches. Here’s a bold, outside-the-box plan that may surprise you: Step 1: Establish a Global Consciousness: Educational Overhaul: Revamp global educational systems to foster empathy, understanding, and appreciation for diverse cultures, religions, and viewpoints from a young age. Step 2: Eradicate Poverty and Inequality: Universal Basic Assets (UBA): Implement a Universal Basic Assets program, where every person on Earth is granted a share of global resources. Step 3: Create a Single Global Governance Entity: World Federation: Establish a democratically elected World Federation that respects regional autonomy but has overriding authority on global issues like…

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Just had a random idea: What if you train a video generating AI, but feed it videos that are reversed? You could show it an image of a crashed car, and it would generate a video of the crash. Show it a broken vase, it would "repair" it. It could one day become like the "reconstruct crime scene" in Detroit: Become Human. What are your thoughts about this? submitted by /u/FluffyIllustrator805 [link] [comments]

A Nature survey of over 1,600 researchers reveals that AI tools are becoming increasingly common in science and are expected to be 'very important' or 'essential' in the next decade. Scientists express concerns about how AI is transforming research, including reliance on pattern recognition without understanding, bias in data, fraud, and irreproducible research. The survey shows that AI tools provide faster ways to process data, speed up computations, and save time and money. Among researchers who use AI, more than one-quarter believe AI tools will become 'essential' to their field in the next decade. Large language models like ChatGPT are mentioned as both impressive and concerning examples of AI tools in science. Source : https://www.nature.com/articles/d41586-023-02980-0 submitted by /u/NuseAI [link] [comments]

I'm looking for a (free) ai image generator like Artbreeder Mixer, that has functions that allow you to "morph" or mix images together via text prompts. Ive looked at a bunch already, and even tried adding the text of the different types in the prompts, bu Ive been getting separated results (like "cat" , "man", "head" wont combine the man and the cat, but rather give me un-morphed results, like a regular man, plus a cat in a suit with no human features. I even get a result with a man standing behind a cat! Ive tried StarryAI, imagecreator, wepik, cant afford midjourney or paid ones right now, some others I cant remember with no mixing... Artbreeder's interface, you can keep adding and it will mix them together. I made these images and others like them very easy in Artbreeder, but its plan is very limited - I could buy more credits, but I need to wait a few days (new job, not paid yet, broke today... lol): ​ morph between man and donkey Morph between angry rapper and gorilla SO, if anyone can suggest some free, or almost free (generous newbie credits?) that can do mixes like this - please point me in the right direction. submitted by /u/magusat999 [link] [comments]

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Artificial intelligence (AI) is being used to create professional-looking profile pictures for job hunting websites like LinkedIn. Apps like Remini, Try It On AI, and AI Suit Up use AI-based software to generate slick profile photos that mimic the work of expert photographers. Users upload multiple selfies, and the AI software creates artificial photos with different hairstyles, clothing, and backdrops. While some find the results realistic, others think they look artificial. The AI services are popular because they are cheap or free, making them accessible to those who can't afford professional headshots. However, opinions are divided on whether AI-generated photos are beneficial or detrimental to self-esteem. Some believe that AI-generated photos allow individuals to put their best self forward and potentially increase their chances of being considered for opportunities. Others worry that relying on AI-generated photos may negatively impact self-worth and confidence. Recruiters generally do not consider whether a photo is AI-generated when evaluating job applications. Source : https://www.bbc.co.uk/news/business-67054382 submitted by /u/NuseAI [link] [comments]

Hi, im currently filming a documentary, but I’m so busy filming, i don’t have time to write notes on footage for the editor. Does anyone know of any ai tool that can help with this and save time and streamline this process? King regards submitted by /u/Brand0n_C [link] [comments]

Hey folks, how would you guys see the effect of AI? Will the small softwares companies will go bankrupt? Since the lots of software are using tools like ChatGpt, Midway Journey etc. It just the starting of new AI technology era which will evolved over the years. In that time we will see more and more AI software which will likely provide efficient and better solution as compare to traditional and open source software. So my question is how do you guys see this? Will small software companies or open source software programs days are number? submitted by /u/Haziq12345 [link] [comments]

Opera has launched Opera One — a new version of the browser that comes packaged with an AI-powered chatbot called Aria.[1] Adobe is going all in on AI, announcing three new generative AI models today that add powerful features to Illustrator and Adobe Express and vastly improve Photoshop’s text-to-image capabilities.[2] ‘South Park’ to Tackle AI for Next Event Special, Releases Teaser.[3] World’s first AI tutor launched in Australia to help students get through their exams.[4] Sources: [1] https://www.theverge.com/2023/6/21/23768888/opera-one-browser-aria-ai-assistant-chatbot [2] https://www.theverge.com/2023/10/10/23911114/adobe-max-firefly-generative-ai-model-photoshop-illustrator-express [3] https://www.hollywoodreporter.com/tv/tv-news/south-park-ai-joining-panderverse-1235615276/ [4] https://www.techguide.com.au/news/computers-news/worlds-first-ai-tutor-launched-in-australia-to-help-students-get-through-their-exams/ submitted by /u/Excellent-Target-847 [link] [comments]

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I’m looking for input and advice on tools for web designers. I use Wordpress a lot, Magento some and frequently code by hand in html JavaScript and PHP. I know there are some AI tools out there now but I don’t know which are best and wanted to find out what people thoughts are on this subject. What tools are you using, for what, and why? Thanks! submitted by /u/PowerTarget [link] [comments]

LLMs' tendency to make up false statements (hallucinate) is a major concern. We need ways to inspect whether they really "know" something is true or not so we can reduce hallucinations. In a new paper, researchers found that LLMs contain an internal "truth vector" - an emergent linear structure that represents factual truth values. They had the insight to visualize how GPT represents simple true/false sentences. The true ones clustered together, while false ones clustered elsewhere - suggesting some kind of 'truth direction' in its learned representations. To test this, they trained linear "probes" on one dataset, and found they could generalize to accurately detect truth values in totally different datasets about other topics. They also directly modified the models to add or subtract the identified truth vectors from its processing of statements. This could flip assessments of truth value, showing the vector causally influences reasoning. Together, these findings provide evidence that neural networks can create emergent, linear structures that represent factual truth. This finding could eventually help make AI systems less prone to hallucinations and falsehoods. TLDR: LLMs can create emergent linear representations of truth. This sheds light on how AI represents abstract concepts and could help us reduce hallucinations. Full summary. Paper is here. submitted by /u/Successful-Western27 [link] [comments]  ( 9 min )

I am working on a research topic in Data Engineering. Forgive me if this is a question frequently asked, I couldn't find this specifically in the FAQ. What are good publication tips and journals to publish in? I read through a few journals and all of them are big publications. What if I opt fot some upcoming or other niche (maybe data engineering) journals submitted by /u/Sherbhy [link] [comments]  ( 9 min )

We have a new benchmark out called SWE-bench (arxiv) It challenges LMs to solve real GitHub issues (feature requests & bug reports) from popular Python repos. Answers are validated using unit tests we crawled from those repos. The benchmark at swebench.com/ shows that even the strongest models, such as Claude 2 and GPT-4, get less than 5% accuracy. ​ We are here to answer any questions you may have. submitted by /u/ofirpress [link] [comments]  ( 9 min )

I would like to understand how I can calculate the probability that a sample belongs to the distribution a diffusion model was trained on. Say, I have an image of a car, and I would like to know whether this image belongs to the distribution that is estimated by the diffusion model. So I would like to know the probability between zero and one at the car belongs to this distribution Do you know how I technically can do this? submitted by /u/That_Phone6702 [link] [comments]  ( 9 min )

Hey guys Looking for some ideas. I'm building out a jupyter book that will be a tutorial on how to use a research platform for data analysis and modelling. My PI has given me free liberty over it. I can not think of a good idea to do the analysis and build the model on. It does not need to be complex but should be good enough so that any researcher, student or organization using the platform can get a good idea of how to use it for ML. Any thoughts on a good area to look into? Any recommendations? Note this will be a tutorial and as such an overly complex model is unnecessary. I just can not figure out what to look into so hoping you guys could give thoughts about possible areas in climate, weather and earth science that I could focus on for the tutorial in the jupyter book. submitted by /u/AdditionalFun3 [link] [comments]  ( 9 min )

First time submitting to ARR here. I was quite confused about this paper resubmission thing. I got rejected by EMNLP (submission directly to EMNLP with openreview) a week ago and I am planning to resubmit it to the ARR system (also using openreview). Does this EMNLP submission count as a previous ARR submission that should be mentioned or not? Do I need to withdraw the paper from EMNLP openreview prior to submitting it to ARR openreview? submitted by /u/Icy-Distribution6887 [link] [comments]  ( 9 min )

Hi! Happy to share a project I've been working on for a while: UI-Act https://github.com/TobiasNorlund/UI-Act It's an AI model architecture designed to autonomously navigate and interact with computers using the graphical user interface. Think of it as a co-pilot that "sees" your screen and acts on it, just as a human would. In essence, it's a custom transformer model taking prompt and screenshots as input, with output heads to predict low-level actions i.e. mouse clicks. In the demo, it has been trained to compute simple expressions in a calculator window, using expert demonstrations/behavior cloning. If scaled up appropriately however, it could provide a basis for a general agent to automate arbitrary tasks on a computer. I would be interested in hearing your thoughts on it, and especially with regards to the trend towards general AI agents and assistants (Windows Copilot / Adept ACT-1 / AutoGPT etc). LMs equipped with e.g. function-calling is a trendy approach, that rely on text-based state representations and APIs to take action. In cases where this is unfeasible, UI-based agents might provide a more general alternative. As the agent's interface to the computer is shared with humans, it can be easily taught using expert demonstrations, and require little or no technical expertice. Let me know what you think! submitted by /u/tobibbelfuel [link] [comments]  ( 9 min )

​ Confidence and trustworthiness of Tsetlin Machines. Hi all! Just completed a new chapter in the book An Introduction to Tsetlin Machines: https://tsetlinmachine.org Happy to receive feedback! Abstract: Collaboration can be essential to manage complex projects. One example is building a house. You then need the expertise of carpenters, plumbers, and electricians. Each profession brings unique skills to the table. Similarly, different types of Tsetlin machines can have distinct capabilities. In this chapter, you learn how Tsetlin machines can team up, allowing them to achieve more than they could on their own. The effectiveness of a team relies on recognising each member's strengths and limitations. Appreciating where your expertise stops and where your coworkers' expertise begins is crucial for effective collaboration. We first explore how Tsetlin machines can assess their competence in Section 7.1. Using the vote count from Chapter 1, you learn to measure how confident a Tsetlin machine is when it makes its decisions. It is possible to be highly confident and still perform poorly. To be trustworthy, confidence must be in line with one's capabilities. Therefore, Section 7.1 also covers how to evaluate trustworthiness. Next, in Section 7.2, you discover how to build a team of Tsetlin machines with different skills. By assessing each Tsetlin machine's confidence, you can lean on the confident ones when making decisions. The result is a Tsetlin machine composite - a construction where multiple Tsetlin machines join forces. You can think of it as a composite material, such as epoxy, which reinforces resin with fibres, making it strong, lightweight, and durable. submitted by /u/olegranmo [link] [comments]  ( 9 min )

Hello all, Just wrapped up Task 1.1 for anomaly detection in student dropout rates. Keen for some extra eyes on it. Task Highlights: Data Pre-processing & Normalisation K-Means Clustering Gaussian Anomaly Detector Used PCA for dimensionality reduction Links to the following files: data.csv Task 1.1 - Rubric.pdf Task1.1Script.ipynb https://drive.google.com/drive/folders/17XcjEoYCrDWqf90VVNdkLAkYNdtWWwGu?usp=sharing Would greatly appreciate any feedback! Cheers! submitted by /u/Nook31 [link] [comments]

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Hi everyone, I’m thrilled to share a prototype we've been tirelessly working on. We are developing a virtualization environment for applications, specifically tailored to engineers, designers, data scientists, and researchers. In a nutshell, our platform enables users to run cloud-hosted desktop apps from any device, making it appear as if the applications are installed on their local machines, while they're actually operating on a remote server. The ultimate goal is to obliterate barriers between local and cloud execution, especially for compute-intensive workloads, thereby allowing seamless usage of High-Performance Computing software on the cloud with the scalability to adjust computing resources as per necessity. We’re here to solicit your invaluable feedback on our product video demo. Your insights will not only help us identify any blind spots and enhance our solution but also better understand the needs and preferences of our potential user base. 📽 [https://youtu.be/QR8FWRnPrXM?feature=shared] We're eagerly awaiting your thoughts and appreciate you taking the time to help us refine our product! Thank you! :) submitted by /u/aaron-cesaro [link] [comments]

Hey all, I found Dolly to be a very interesting project when it was released but I'm curious if it has similar value today because a lot of synthetic data generation options seem to be popping up. Now it seems like Dolly is human generated/curated by over 5k employees (which is great), but wouldn't it be a better approach now to have Llama70b (or maybe Falcon) just generate future variants of 15k rows? I havent been able to figure out why we arent seeing more synthetic datasets like this on HF? Is the bottleneck licensing, compute or just incentive? Heres the original Dolly post thread: https://www.reddit.com/r/MachineLearning/comments/120usfk/r_hello_dolly_democratizing_the_magic_of_chatgpt/ submitted by /u/buzzyness [link] [comments]

What is the loss function that needs to be used for a task that takes an input image with a lot of haze and produces an image with reduced haze. The architecture is a simple encoder decoder architecture. I tried MSE as some articles and ML guides say that MSE is good for pixel wise comparison and also tried Categorical Crossentropy but none of them work so great. MSE works but produces artefacts like red/green/ blue spots and spatters and at worse times it produces a white image. The research on this task includes use of SIDNet[Single Image Dehazing Net], Transmission maps, Dark channel prior algorithm, FFA net, etc trained on the Benchmark datasets (RESIDE,SOTS). I aim to create a simple architecture for college project so I chose the Enc-Dec architecture. Any suggestions are appreciated. submitted by /u/Wild_Basil_2396 [link] [comments]

Autonomous systems startup, PassiveLogic, assembled a differentiable computing team, to build a fast systems language with native performance differentiability. Their latest benchmark trains networks two orders of magnitude faster than PyTorch and Tensorflow. See: LinkedIn Post&dashCommentUrn=urn%3Ali%3Afsd_comment%3A(7118052434916110337%2Curn%3Ali%3Aactivity%3A7117911978106355712)) It's a collaborative effort with the Swift community and Apple's compiler team, using the Swift language as a strongly typed embedded language that performs ahead of time compilation of graph neural nets. The focus is on fusing systems programming and AI engineering into a single native high performance language, to enable typed heterogeneous inference and training. The compiler development is open sourced as part of the standard Swift package. Try it yourself at swift.org. submitted by /u/taharvey [link] [comments]  ( 9 min )

I recently tried to refactor a previous project that I had, but I realized that after making all of the changes the performance wasn't reproducible anymore. I decided to start from scratch, make incremental changes, and make sure that the model's performance is maintained with each change. Very basic in hindsight, but I guess I was too hasty with coding. Anyway, running the full model's training and evaluation with each change is proving to take too long. I'm curious if there's any other way that people implement TDD in the context of machine learning since projects/applications tend to be more time consuming then typical applications. submitted by /u/Seankala [link] [comments]

Posted by Nicholas Rubin, Senior Research Scientist, and Ryan Babbush, Head of Quantum Algorithms, Quantum AI Team If you’ve paid attention to the quantum computing space, you’ve heard the claim that in the future, quantum computers will solve certain problems exponentially more efficiently than classical computers can. They have the potential to transform many industries, from pharmaceuticals to energy. For the most part, these claims have rested on arguments about the asymptotic scaling of algorithms as the problem size approaches infinity, but this tells us very little about the practical performance of quantum computers for finite-sized problems. We want to be more concrete: Exactly which problems are quantum computers more suited to tackle than their classical counterparts, an…  ( 94 min )

At one of the U.K.’s largest technology festivals, top enterprises and startups are this week highlighting their latest innovations, hosting workshops and celebrating the growing tech ecosystem based in the country’s southwest. The Bristol Technology Festival today showcased the work of nine startups that recently participated in a challenge hosted by Digital Catapult — the Read article >  ( 6 min )

Put the pedal to the metal this GFN Thursday as Forza Motorsport leads 23 new games in the cloud. Plus, Acer’s Predator Connect 6E is the newest addition to the GeForce NOW Recommended program, with easy cloud gaming quality-of-service (QoS) settings built in to give Ultimate members the best streaming experience. No Breaks, No Limits, Read article >  ( 6 min )

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Most real world applications for RL (robotics, game dev, finance) are in not normally done in Python, yet all major RL frameworks are written in Python. Is there a good/high-performance cross-language framework to do RL in other languages like C++/.Net/Java? If not, do you think people would be interested in such a framework? ​ submitted by /u/xor24 [link] [comments]

Do you know any work that tries to develop RL agents that exploit some sort of high-level model of the problem (it could also be given by an expert human) to learn faster or operate on out-of-distribution scenarios? I'm particularly interested in Causal Models, but any similar thing could be interesting for me submitted by /u/fedetask [link] [comments]

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I've been looking at DrQ-v2 (https://github.com/facebookresearch/drqv2) recently and it samples from replay in a way that seems odd to me but may have a purpose I don't understand. They store experiences in a compressed file by episode, this makes some sense since it means they don't have to store everything in RAM and they delay disk writes until the end of the episode so they don't slow down the sim operation. On sampling, they randomly select an episode then randomly select an experience from the episode, calculating the n-step reward dynamically at sample time instead of at experience storage time. This is then fed to the model by a pytorch DataLoader. This means a _lot_ of disk reads during the optimization step which can't be ideal but I'll put that aside. What is the advantage of doing this selection by episode? It may give a better spread across episodes in each update, but I'm not sure that makes up for the potential downsides of making prioritization and other replay tricks much harder. Any ideas? submitted by /u/EDMismyO2 [link] [comments]

I have a very simple observation: a list of random value state = [random.uniform(-0.2, 0.2) for _ in range(200)] reward = state * actions . The reward is not using the next state, it's using the previous state i gave to the model. So basically i already give the answer to the model, the best action is : if state > 0 action =1, if state < 0 action = -1 I tried using PPO, but it seem not learning at all. My test_env.py is here: ``` import gymnasium as gym import numpy as np from gymnasium import spaces from gymnasium.utils import seeding from stable_baselines3.common.vec_env import DummyVecEnv import random class TestEnv(gym.Env): metadata = {"render.modes": ["human"]} def __init__( self, item_count, test_steps, is_train = True, ): self.is_train = is_train self.test_steps = test_step…

These research papers were presented at the IEEE Symposium on Visual Languages and Human-Centric Computing (opens in new tab) (VL/HCC 2023), a premier forum for design, theory, and application of computing technologies for programming, modelling, and communication. Large language models (LLMs) have revolutionized the way novice programmers and everyday computer users tap into the capabilities […] The post Microsoft at VL/HCC 2023: Focus on co-audit tools for spreadsheets appeared first on Microsoft Research.  ( 10 min )

This post is a follow-on to a discussion that started on Twitter yesterday. This tweet must have resonated with a lot of people because it’s had over 230,000 views so far. You almost have to study advanced math to solve basic math problems. Sometimes a high school student can solve a real world problem that […] Homework problems are rigged first appeared on John D. Cook.  ( 7 min )

The advent of generative AI is empowering everyone alike – organizations, small businesses, individuals, students, and medical professionals, to name a few. The last couple of years have been revolutionary for artificial intelligence innovation and transformation. How will 2024 shape up for AI, AI tools, and related professionals? Let’s analyze the trends that are most… Read More »12 Generative AI Trends to Watch Out for The post 12 Generative AI Trends to Watch Out for appeared first on Data Science Central.  ( 20 min )

Top dating apps Tinder, Hinge and Bumble have all stated that they're already investing in AI to make their apps better. They're using it to verify profiles, match people based on bios and interests, and help generate profile descriptions and liven conversations. But what about machine learning on user photos? iris Dating uses AI to analyze user input in the form of liking or disliking faces ("swiping" profiles). We all know if we like blondes or brunettes, blue or brown eyes, short or long hair, beard or no beard, etc. But AI can pick up the subtlest features (proportions, distances, curvatures etc.) and build a face map. A matrix of features, if you will. It doesn't just look for a person looking like your favorite celebrity crush. It understands what you're really attracted to. From there it's an easy path: if it knows which features attract me, it can predict my level of attraction to a specific individual (specifically, their face). Find the persons with the highest predicted attractiveness (for me, not for everyone), rank them by attraction for me, and we have a potential high mutual attraction match. The two stats I have are that on average women like 55%(!) of the profiles iris picks for them; and that users have 40x higher chances of matching when they've trained the model to understand their taste. I know it takes a lot more than a pretty face to make for a great relationship, but it sure doesn't hurt to start with strong physical attraction. Missed connections on Craigslist are about just that: seeing a face you can't forget. Find me more of these "wow" faces and let's go from there. What do you think? Is it too early? Too bold? Too niche? submitted by /u/akahamlet [link] [comments]

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The AI research job market is going through a shakeup, with a high demand for skilled researchers and a scarcity of talent. Companies closely monitor the movements of researchers as an indicator of their ability to transition from concept to product. The market is highly competitive, with researchers being offered high salaries and compensation packages. This has led to high turnover and attrition in many companies, causing unsettledness among employees. Despite the challenges, the investment in AI research is expected to drive innovation and push the boundaries of the Transformer architecture. Source : https://www.interconnects.ai/p/ai-research-job-market submitted by /u/NuseAI [link] [comments]

I'm looking for ways to create art for a game I'm creating. submitted by /u/Yenii_3025 [link] [comments]

Transformers are great at NLP and computer vision tasks, but I was surprised to learn they still lag behind simple linear models at time series forecasting. The issue is how most Transformer architectures treat each timestamp as a token and fuse all the variable data from that moment. This makes two big problems: Variables recorded at slightly different times get blurred together, losing important timing info Each token can only see a single moment, no long-term dependencies So Transformers struggle to extract useful patterns and correlations from the data. Some researchers from Tsinghua University took a fresh look at this and realized the Transformer components themselves are solid, they just need to flip the architecture for time series data. Their "Inverted Transformer" (or iTransformer): Makes each variable's full history into a token, instead of each timestamp Uses self-attention over variables to capture relationships Processes time dependencies per variable with feedforward layers This simple tweak gives all the benefits we want: State-of-the-art forecasting accuracy, beating both linear models and standard Transformers Better generalization to unseen variables Increased interpretability Ability to leverage longer historical context TLDR: Inverting Transformers to align with time series structure allows them to outperform alternatives in working with time series data. Full summary. Paper is here. submitted by /u/Successful-Western27 [link] [comments]

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I guess I'm looking for the impossible but does anyone know of a generator that has all of the following qualities in order of importance least to most important: Has a massive variety of styles like Womba's private discord server does. "Create variants" function like how a Womba discord personal server generator allows you to do. Generates beautiful "digital art" style images like the digital https://www.unstability.ai/ does. (Man those images are pretty) faces are really good most of the time. (It's frusterating as it looks so good but I can't seem to get any group sex poses going on.) Provides a variety of poses such as https://easywithai.com/ai-image-generators/promptchan-ai/ which also allows you to upload you own images for poses, like how I could upload a real life orgy image and as long as it could distinguish the bodies as being separate (not a big pile of limbs) it does pretty good, but lacks severely lacks in facial quality. Like a big booty girl in hyperreal style 1080P or higher resolution. (Again Womba is good here, but they are just extreme on their restrictions.) 1080P should be the minimum for any paid service as how can we truely enjoy a full screne image on anything less without it pixeling out? Doesn't cost $150/month (yes I found one that does all this but their premium subscription cost like $150/month (seduced.ai) and it's not even unlimited. I paid $90 for a full year at Womba discord unlimited but again, $150/month is just not worth it. If anyone knows of a server that has all these for around $25/month or less, please let me know. If really appreciate it. submitted by /u/russader [link] [comments]

I have a high resolution black and white print and a generic quality colour image of the same photo, that I'd like AI to look at both images and make the B&W into colour. Is this possible? submitted by /u/NikonD3X1985 [link] [comments]

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Cybersecurity firm Avast is calling out a long-lived tool “LoveGPT,” that has haunted popular dating apps and that has been upgraded with artificial intelligence, gaining the ability to build fake profiles and manipulate unsuspecting users.[1] The outsider told the WSJ that Microsoft used AI from its partner OpenAI, which was then used to launch GitHub Copilot at $10 per month, but lost $20 per user in the average six months on average in the first 2023. Some Copilot users cost as much as $80 per month.[2] SK Telecom said on Monday that it successfully wrapped up its international AI competition of 226 teams, “Prompter Day Seoul 2023,” held in partnership with OpenAI.[3] Google DeepMind Researchers Introduce Promptbreeder: A Self-Referential and Self-Improving AI System that can Automatically Evolve Effective Domain-Specific Prompts in a Given Domain.[4] Sources: [1] https://decrypt.co/200787/lovegpt-ai-dating-apps-catfishing-hack-avast [2] https://game-news24.com/2023/10/10/microsoft-lost-20-for-every-10-copilot-ai-subscription-report-45-for-every-10-copilot-ai/ [3] https://asianews.network/skt-openai-hold-ai-competition-for-social-good/ [4] https://www.marktechpost.com/2023/10/08/google-deepmind-researchers-introduce-promptbreeder-a-self-referential-and-self-improving-ai-system-that-can-automatically-evolve-effective-domain-specific-prompts-in-a-given-domain/ submitted by /u/Excellent-Target-847 [link] [comments]

VIDEO EDITING InVideo CapCut Filmora Veed io Rotor KEYWORD RESEARCH VidiQ Summarized YT Summary CONTENT CREATION Explore Al Vidds Opus Descript Lumen5 Steve Al AUDIENCE ENGAGEMENT ManyChat TubeBuddy Canva Hootsuite ANALYTICS Vidyo Nova Al Daily Life Tools Taskade TLVD Bardeen Al Vondy Al Notion Al Chatbots Tools YatterPlus Typewise Quickchat Cohere Kaizan Coding Tools Durable Al 10Web Akkia Replit Deepcode Design Tools Flair Al Autodraw StockIMG Booth Al Clipdrop Content Creation Tools Writesonic Beautiful Al Tome Al ChatABC Steve Al Music Tools Boomy Amper Jukedeck Melodrive BrainFM Writing Tools AISEO Quillbot Writesonic Bertha Al Simplified Youtube Tools Eightify Thumbly Steve Al ClipMaker TubeBuddy Twitter Tools Tweetmonk Tribescaler Postwise Tweetlify Tweethunter Sales Tools Lavender Warmer Regie Twain Octane Marketing Tools simplified ContentEdge Copt Smith Copy Al Mutiny Research Tools Consensus Paperpal Trinka Writesonic scholarcy I'm just sharing my experiences and observations in the field of ai. LIST AND SITE submitted by /u/PerceptionPlayful469 [link] [comments]

Awesome ChatGPT Prompts submitted by /u/Senior_tasteey [link] [comments]

Hello, first time using Google Colab and huggingface datasets. Colab notebook is easy to setup but I can't seem to figure out how to download datasets from huggingface. I am trying to download https://huggingface.co/datasets/kili-technology/plastic_in_river dataset in Colab Notebook. After reading some beginners forums, I modified the example to look like one below but it failed. from datasets import load_dataset data_files = {"train": "train.csv", "test": "test.csv", "validation": "validation.csv"} dataset = load_dataset("kili-technology/plastic_in_river", data_files=data_files) Because there's no path to the files to be downloaded. Can someone explain how to download datasets from huggingface please? Downloading builder script: 100% 3.25k/3.25k [00:00 in () 2 3 data_files = {"train": "train.csv", "test": "test.csv", "validation": "validation.csv"} ----> 4 dataset = load_dataset("kili-technology/plastic_in_river", data_files=data_files) 5 frames /usr/local/lib/python3.10/dist-packages/datasets/data_files.py in resolve_pattern(pattern, base_path, allowed_extensions, download_config) 366 if allowed_extensions is not None: 367 error_msg += f" with any supported extension {list(allowed_extensions)}" --> 368 raise FileNotFoundError(error_msg) 369 return out 370 FileNotFoundError: Unable to find 'https://huggingface.co/datasets/kili-technology/plastic_in_river/resolve/main/train.csv' submitted by /u/0ni0nrings [link] [comments]  ( 9 min )

I've recently been trying to pre-train my own small language model on the tiny-series datasets on huggingface. I also wanted to use a model similar to MEGABYTE but I don't understand how using bytes would work. The only implementation I could find from lucidrains used str(chr(max(32, token))) to decode any token (byte) to a character and put the embedding size as 256. Firstly, why 256 and not 256-32 as any values below 32 are ignored? Also, many byte-level models including this and ByteT5 mention that they can process any text sequence even in a multilingual setting, however how would that be true if we are only using one byte, would we have to move to 2 bytes or use an UNK token, and if we did use 2 bytes that would make our embedding size around 65000 which defeats sort of the point as o…  ( 10 min )

Consider a predictive model that is predicting if an outcome Y will occur in Q1 2023, based on data from Q1 2022. Now, if want to predict outcomes for 2024, we must use last years data to build the model, but we are going to have some bias if there are features that vary year over year. Is the best approach in such a situation to try and tune/validate the model with other years in the hopes of mitigating any features that are correlated with a specific year? Any help would be much appreciated, as I can't find agreed upon methods. submitted by /u/unga123 [link] [comments]  ( 9 min )

I have a goal and am looking for direction from others who know more than me about machine learning. I want to submit 5-10 pieces of text to a model. The text will be anecdotes from a common experience but each one from a different person’s perspective. For example, if a family visits a theme park, each family member will have a story or two about the day. Each family’s story would be a submission to the model. One person might have loved the roller coaster and can tell about the exciting parts. Another person maybe just can’t stop talking about how great he food was. Someone else maybe felt sick and complains the line at the bathroom was too long. Perhaps another family member also rode the same roller coasters as the first person but instead hated it, so would have a very different description of it than the first. All these anecdotes are submitted to the model. Then, the model can be queried. Such as, “Tell me about the theme park.” or “I love roller coasters. Tell me about the theme park.” or “I tend to overeat, tell me about the theme park.” (the model wouldn’t hype of the food, maybe it would talk about how much exercise the visitors get by walking around all day.) In this case of a theme park context, the model would have a preconception of a theme park. It would know the general concept, know of several examples or standards that it could compare this theme park against, understand it’s all for fun, etc. This type of model may be available as an API or model already and I just don’t know about it. That’d be fine, please point me towards it. Or, maybe there’s something already available but would need tweaked or customized. submitted by /u/Semper_Disco [link] [comments]  ( 10 min )

Can you give easy to understand sources and hands-on practice methodology to master ML? Help me understand build the models in and out . Thank you submitted by /u/the_mystic_1 [link] [comments]  ( 9 min )

Over Feb 12-13 of 2024, the National Science Foundation (NSF) is sponsoring a workshop titled “Integrating LLMs into the Materials Chemistry Curriculum” in Golden, Colorado. We aim to explore and develop innovative ways to incorporate large language models (LLMs, e.g. GPT, ChatGPT, and Bard) into upper division chemistry laboratories and virtual lab experiences. During the workshop, participants will brainstorm and create demonstrations incorporating LLMs into the curriculum. The event will bring together folks across academia and the private sector with disciplinary backgrounds that range across chemistry, computer science, materials science, physics, and education. There is no registration fee, and we anticipate being able to cover the majority of participant travel costs thanks to NSF support. Participants early in their career (i.e., graduate students, postdoctoral scholars) are particularly encouraged to apply. If you are interested in participating in this workshop, please fill out the Google form (link below). Please feel free to distribute this invitation widely. Application: https://forms.gle/P9QdNiCuaUAHFZj29 submitted by /u/KC2792 [link] [comments]  ( 9 min )

Hello, I'm a developer with 6 years of experience in the mobile field, and I recently completed my master's degree in artificial intelligence (Text mining). I want to transition into the field of AI, but I need more experience with projects in the "real world," outside of academia, and I'd like to contribute to an open-source project. I looked on Github, but I ended up feeling confused and not sure where to start. P.S.: I did some research in this subreddit, but the posts about contributions seemed a bit dated. submitted by /u/Substantial_Fact_205 [link] [comments]  ( 9 min )

Video: https://youtu.be/0m12vkaoE7w ​ Detailed Medium post will follow in the upcoming days. https://medium.com/@pssdplayer submitted by /u/HistorianCrafty3514 [link] [comments]  ( 9 min )

I have collected over 20 million shopify products & had the following ideas for them: - LLM ( Finetune an llm to know how to speak ecom ) - Video bot that can make videos on those products, using their description, elevenlabs & AIFaceGen - EcomStore that will markup the products about 30% ( This will need the bot to frequently scrape, to ensure that the products are up to date ) - Selling the dataset based on fragments, like 1$ per 1k-10k records, depends on what sells. Please let me know if these are good ideas, and if someone would like to support / help me in any way ( I just need to selfhost my supabase instance, & add all the products to it & then dev can get started ) submitted by /u/AdonisCodes [link] [comments]  ( 9 min )

As the title says I’m looking for an open-source AI model for generating question-and-answers with a correct answer option and explanation to the correct answer from the input context. So far I have tried these models, TheBloke/Llama-2-7B-GPTQ TheBloke/Llama-2-13B-GPTQ TheBloke/Llama-2-7b-Chat-GPTQ (the output is not consistent. Sometimes I get an empty response or without the correct answer option and an explanation data) TheBloke/Llama-2-13b-Chat-GPTQ (even 7b is better) TheBloke/Mistral-7B-Instruct-v0.1-GGUF(so far this is the only one that gives the output consistently. But not able to generate more than 2 QA due to max token limit of 512. Even tried setting the max token as 1024, 2048 but nothing helped) TheBloke/Mistral-7B-OpenOrca-GGUF NousResearch/Llama-2-7b-chat-hf My system configurations are: Windows 10 with 16GB GPU Additional Information: The input prompt token will be around 250-350 tokens per request. submitted by /u/gokulcv [link] [comments]  ( 9 min )

I want to build a model to predict churn in a third party logistics company. What variables should make up my data? Any help would do. Thanks submitted by /u/DisastrousAd8814 [link] [comments]  ( 9 min )

Hello everyone, I have a specific online vectorization use case: I'm looking to search the internet for articles, vectorize these articles along with the search queries, and then retrieve the most relevant passages from them. Currently, I have basic hosting through DigitalOcean. Could anyone recommend the most suitable vector dataset for this task? Additionally, considering my resources, is it feasible to run this system solely on CPUs? And if so, would this setup be scalable if deployed on CPUs only? submitted by /u/Traditional-Poet2746 [link] [comments]  ( 9 min )

Hi all, for a text work of mine I am trying to do a project based on generating digital twin of networks. My goal is to create a digital twin of a network and then work on it from a cyber security point of view. I will briefly explain what I would like to do. I am currently using software for network vulnerability scans (OpenVAS). I use this software to perform network vulnerability scans at the network level, so basically to OpenVAS I pass a network (for example 192.168.xx.xx/24) to automatically identify all the vulnerabilities that are there. The next step ( what I'd like to do and that's why I'm asking for your advice) is to create a digital twin of the newly scanned network and then perform a penetration test on this digital twin of the network, without going to stress the actual network. Ideally, I would like to pass the output of the OpenVAS vulnerability scans, routing rules, and firewall rules to some tool that will then generate for me the digital twin of the network, which will then be used for offensive cybersecurity, so exploits, privilege escalation, etc.... will be tested on this digital twin without worrying about breaking some kind of service or stressing the real network. What I am asking is, do you know of any tool that would do the trick for me? So some tool that allows me to generate a digital twin of a network by providing as input vulnerability scans (xml,json,csv etc...), routing rules, firewall rules, pcap traces etc... Do you have any references or documentation? Are you aware of any open source tools? I thank you for your helpfulness! ​ submitted by /u/Salt-Arugula-8128 [link] [comments]  ( 9 min )

Quick intro Lineups are one of the first steps in the VFX pipeline Source: - orignal footage that was shot on set - a reference (quicktime) video from the film edit. Task: The reference shows modifications to the original footage. They can be : - timewarp (either fixed retimes like 200% speed or completely random) - transform (moved the image in x/y axis, rotation, scale, etc.) So the lineup task is to align the original footage to the reference quicktime. What I did so Far: Made a simple script in the software Nuke, using some Python and readily available tools to make it work on a simple shot. General logic is compare every frame and the associated one is the frame with the least difference between the two. This works on super simple and straightforward tasks. (can provide more info if needed). Issue: Some references are more heavily modified. They can have some muzzle flash, basic 3d objects or even some slight error introduced like a distortion applied to the image when none shouldn't so it will never be perfectly aligned. This makes the difference of the full frame higher for some frames, making the lineup wrong. (it will take the wrong frame that has no muzzle flash, because it has less difference...)Some other things to consider is that watermarks are covering the ref and the colors are not perfectly matching, can get them close enough, but there's a difference. Conclusion: Because of those issues, I'm thinking about using Machine Learning. I have next to no knowledge on the subject. I know there Is a bunch of ways to train a model, but no clue where to start, so here's my question : Which learning styles has the best potential to be able to solve this task? submitted by /u/Pretty_Customer_8113 [link] [comments]  ( 9 min )

I will have to pick and start a research project next January for my final year. So wanted to start exploring now. I want to do something substantive and interesting enough to get published. submitted by /u/BadMeditator [link] [comments]  ( 9 min )

submitted by /u/hardmaru [link] [comments]  ( 8 min )

Transformers are great at NLP and computer vision tasks, but I was surprised to learn they still lag behind simple linear models at time series forecasting. The issue is how most Transformer architectures treat each timestamp as a token and fuse all the variable data from that moment. This makes two big problems: Variables recorded at slightly different times get blurred together, losing important timing info Each token can only see a single moment, no long-term dependencies So Transformers struggle to extract useful patterns and correlations from the data. Some researchers from Tsinghua University took a fresh look at this and realized the Transformer components themselves are solid, they just need to flip the architecture for time series data. Their "Inverted Transformer" (or iTransformer): Makes each variable's full history into a token, instead of each timestamp Uses self-attention over variables to capture relationships Processes time dependencies per variable with feedforward layers This simple tweak gives all the benefits we want: State-of-the-art forecasting accuracy, beating both linear models and standard Transformers Better generalization to unseen variables Increased interpretability Ability to leverage longer historical context TLDR: Inverting Transformers to align with time series structure allows them to outperform alternatives in working with time series data. Full summary. Paper is here. submitted by /u/Successful-Western27 [link] [comments]  ( 9 min )

Hi! The task is to train N TensorFlow/Keras models using [2, ... N] GPU's on K different datasets in parallel. It is for testing a custom pipeline, you create a pipeline, you run it on multiple different datasets and get an aggregated metric. For now I'm using a for loop but how do I do it in parallel e.g. on AWS? I googled, but surprisingly haven't found a lot of results. I looked at Apache AirFlow because I'm vaguely familiar with it but so far I couldn't get a definite answer on how it works with multiple GPU's. Second option I found is to use Ray library. Is it worth trying? What should I use to solve this task? Thanks. UPD. I'd also consider a PyTorch solution as a backup option. UPDUPD. Jesus, why Reddit removing newlines after edit? submitted by /u/Disastrous_Sky9468 [link] [comments]  ( 9 min )

My team and managers are so easy to be with. Very grateful for that. The pay is okay. 150k/yr TC in Midwest. Hard for me to make a switch given how much I am appreciated. I almost feel spoiled when it comes to flexibility. I have overachiever tendency and the pace is so slow in adopting my ML models. I am the “lead”/senior data scientist in an R&D supporting scientists decision making with machine learning. Importantly, I am in a huge multinational consumer product company and I am not in the Data science organization, I bridge between the two and the data science expert on the team. I have developed the domain expertise and I have a PhD in an applied computational field with 5 years experience . I am not as challenged with getting deeper into complex stats, I have been really honing the soft skills of communication, influencing etc so getting comfortable in a senior role. Also I have been growing as a ML engineer building my own pipelines and deploying my models on prem server that they bought for me. I am not sure how greener it is on the other side, how do senior folks approach deciding when to move on? Any input is much appreciated. submitted by /u/Diligent_Trust2569 [link] [comments]  ( 9 min )

I have 200k observations overall. I split my data into training and test set. My target variable has low prevalence ~ 9% so I tried random oversampling, random undersampling and SMOTE. After I fit my models, I tested them on my training test and the results were awful. I mean I've never had a model with 50% roc-auc, but then again, I rarely developed ML models. I'm wondering what the next steps would be? I understand there could be some sort of overfitting. But what would you do next? Any references would be appreciated :) submitted by /u/Actual-Muscle-9846 [link] [comments]  ( 9 min )

Given an image, and an audio file (TTS generated), what is current fastest library that can output me a video of a talking image with the audio on it? I have made some research and I have seen Wav2Lip and SadTalker. Any better options? I am looking for processing speed and for the lesser hardware intensive solution for a side project. Thanks! submitted by /u/reddit2vid [link] [comments]  ( 9 min )

Hello everyone! Here is my pet project, https://www.loopquest.ai/. I am trying to build a platform like Github to let people upload their simulation environments so people can train their AI agents by interacting with the environments created by others. Here is a 2-min demo, https://youtu.be/d53NFjkU7JA. It is not launched yet but would love to get some early feedbacks. Here is the corresponding Github repo https://github.com/LoopMind-AI/loopquest. For now, the package can log env-agent interaction data by adding one extra line of code. You can think of it similar to https://github.com/google-deepmind/envlogger but with much better backend and frontend support. Any feedbacks are appreciated :) submitted by /u/jxx123 [link] [comments]  ( 9 min )

The pipedream-2bw paper and the Zero-offload paper both show that 1-step delayed asynchronous gradient update doesn’t affect the convergence (and perplexity) while improve the training efficiency (by fully utilize the bubbles in pipeline parallelism) at a large margin. However, both the Megatron-LM and the DeepSpeed don’t use pipedream-2bw scheduling. Could anyone share me some insights or ideas about why such an efficient scheduling scheme doesn’t get popular in the LLM pretraining community? Does it suffer convergence/accuracy issue in practice? Or are there any other concerns that blocking it become the default / most popular pipeline parallelism scheduling? (I posted the same question in hacknews as well: Why async gradient update doesn't get popular in LLM community? | Hacker News) I have tried to implement the pipedream-2bw scheduling scheme on Megatron-LM and do can reproduce the performance gain as well as loss convergence with GPT-2 345M using 8xV100 GPUs: https://github.com/sighingnow/Megatron-LM/blob/ht/dev-pipe/megatron/core/pipeline_parallel/schedules.py#L1421 submitted by /u/sighingnow [link] [comments]  ( 9 min )

What’s the best IDE to work with or is it on user needs that determines best fit or is their one top dog and dominator that can robustly if not better preform other IDE’s ? submitted by /u/External_Age_5855 [link] [comments]  ( 9 min )

submitted by /u/zezeartix [link] [comments]  ( 8 min )

Hi, I want to train a neural network to calculate Shapley values based on a given characteristic function. Depending on a given characteristic function, calculated through a dedicated algorithm, Shapley values can be any number, positive or negative, without a set range. Because of this, I am unsure, for the specific application of calculating Shapley values, what activation function to use in a neural network that would calculate them. The relu function, as well as leaky relu function, either cannot give values that are negative or have trouble giving large negative values, and sigmoid or tanh can only give values in a certain range. I am aware that there are other commonly used activation functions, but all the ones I could find had one of these issues, which would make training a network to calculate Shapley values difficult. Any advice? submitted by /u/PowNotBigSurprise [link] [comments]  ( 9 min )

I was thinking to create an app based on style gan which will include facebook , instagram theme and style transfer it with profile pic so shall i create this app or not .I want to know if it will be good idea. submitted by /u/No_Claim_8651 [link] [comments]  ( 9 min )

What is the optimal framework and configuration for hosting large language models (LLMs) for text-generating generative AI applications? Despite the abundance of options for serving LLMs, this is a hard question to answer due to the size of the models, varying model architectures, performance requirements of applications, and more. The Amazon SageMaker Large Model Inference […]  ( 13 min )

In this post, we show how to index information stored in websites and use the intelligent search in Amazon Kendra to search for answers from content stored in internal and external websites. In addition, the ML-powered intelligent search can accurately get answers for your questions from unstructured documents with natural language narrative content, for which keyword search is not very effective.  ( 7 min )

The last couple article have looked at various kinds of mean. The Python code for four of these means is trivial: gm = lambda a, b: (a*b)**0.5 am = lambda a, b: (a + b)/2 hm = lambda a, b: 2*a*b/(a+b) chm = lambda a, b: (a**2 + b**2)/(a + b) But the arithmetic-geometric mean […] Python code for means first appeared on John D. Cook.  ( 5 min )

in an earlier post I said that the arithmetic mean of two frequencies an octave apart is an interval of a perfect fifth, and the geometric mean gives a tritone. This post will look at a few other means. Intervals The harmonic mean (HM) gives a perfect fourth. The arithmetic-geometric mean (AGM) gives a pitch […] More ways of splitting the octave first appeared on John D. Cook.  ( 6 min )

This afternoon I wrote a brief post about Terence Tao’s new paper A Maclaurin type inequality. That paper builds on two classical inequalities: Newton’s inequality and Maclaurin’s inequality. The previous post expanded a bit on Newton’s inequality. This post will do the same for Maclaurin’s inequality. As before, let x be a list of real […] Maclaurin’s inequality first appeared on John D. Cook.  ( 5 min )

The previous post mentioned Newton’s inequality. This post will explore this inequality. Let x be a list of real numbers and define Sn(x) to be the average over all products of n elements from x. Newton’s inequality says that Sn−1 Sn+1 ≤ S²n In more terminology more recent than Newton, we say that the sequence […] Newton’s inequality and log concave sequences first appeared on John D. Cook.  ( 5 min )

Research Focus: Principal researcher Lester Mackey recognized for pioneering statistical and ML techniques; Pareto frontiers in neural feature learning; structural inequality in the influencer industry; new research on cardinality estimation. The post Research Focus: Week of October 9, 2023 appeared first on Microsoft Research.  ( 9 min )

Developers have a new AI-powered steering wheel to help them hug the road while they drive powerful large language models (LLMs) to their desired locations. NVIDIA NeMo SteerLM lets companies define knobs to dial in a model’s responses as it’s running in production, a process called inference. Unlike current methods for customizing an LLM, it Read article >  ( 6 min )

Hi! I'm new to Mujoco and robot dynamics. When I read the Mujoco document, I'm confused about the gainprm and biasprm parameters. I want to understand the meaning of these parameters and tune the actuation speed of my actuator. An easy-to-understand explanation or supporting material would be appreciated. Thanks in advance. submitted by /u/UpperSearch4172 [link] [comments]

Hello everyone! Here is my pet project, https://www.loopquest.ai/. I am trying to build a platform like Github to let people upload their simulation environments so people can train their AI agents by interacting with the environments created by others. Here is a 2-min demo, https://youtu.be/d53NFjkU7JA. It is not launched yet but would love to get some early feedbacks. Here is the corresponding Github repo https://github.com/LoopMind-AI/loopquest. For now, the package can log env-agent interaction data by adding one extra line of code. You can think of it similar to https://github.com/google-deepmind/envlogger but with much better backend and frontend support. Any feedbacks are appreciated :) submitted by /u/jxx123 [link] [comments]